System and method for message clustering

ABSTRACT

The disclosure describes systems and methods delivering communications associated with delivery conditions in which the occurrence of the delivery condition is determined by monitoring information received from a plurality of sources via multiple communication channels. The message delivery systems allow messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. A message (which may be any data object including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) is delivered in accordance with delivery conditions based on any available data, including topical, spatial, temporal, and/or social data. Furthermore, because the systems coordinate delivery of messages via multiple communication channels and through multiple devices, the communication channel for delivery of a message may be dynamically determined based on the delivery conditions.

BACKGROUND

A great deal of information is generated when people use electronic devices, such as when people use mobile phones and cable set-top boxes. Such information, such as location, applications used, social network, physical and online locations visited, to name a few, could be used to deliver useful services and information to end users, and provide commercial opportunities to advertisers and retailers. However, most of this info-nation is effectively abandoned due to deficiencies in the way such information may be captured. For example, and with respect to a mobile phone, information is generally not gathered while the mobile phone is idle (i.e., not being used by a user). Other information, such as presence of others in the immediate vicinity, time and frequency of messages to other users, and activities of a user's social network are also not captured effectively.

SUMMARY

People often create numerous messages in connection with related topics, events or things, but these messages are often disparate in time, form, communication channel, subject header, or sender/receiver communication handles. Using information and relationship processing, as described further herein, embodiments of this disclosure are described that identify and pull together messages into useful and meaningful clusters that are readily available to a user, and actionable by the system and users, to enhance the management and information extraction potential of the myriad messages created and handled by people and processes oil a daily basis.

In conventional messaging systems, when a message is sent by a sender, whether an electronic message such as an e-mail, a recorded voicemail message, an instant message and other types of electronic messages that may be addressed by a sender to one or multiple recipients, the message is received by each recipient in essentially the same form. In other words, the contents of the message are received by the recipient(s) in the form in which they were sent by the sender. In some instances, known electronic mail systems may insert advertising messages or background messages, which may or may not be under the sender's control, but in any event conventional messaging systems heretofore known do not utilize information related to the sender and/or each recipient of the message in order to individually tailor and create an augmented message, the augmented message containing information in addition to the information intended by the sender, and which augmented information is derived by the network sending the message based upon information known to or available to the network about the message as well as the sender and/or each recipient of the electronic message.

This disclosure describes systems and methods for using data collected and stored by multiple devices on a network in order to improve the performance of the services provided via the network. In particular, the disclosure describes systems and methods delivering communications associated with delivery conditions in which the occurrence of the delivery condition is determined by monitoring information received from a plurality of sources via multiple communication channels. The message delivery systems allow messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. A message (which may be any data object including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) is delivered in accordance with delivery conditions based on any available data, including topical, spatial, temporal, and/or social data. Furthermore, because the systems coordinate delivery of messages via multiple communication channels and through multiple devices, the communication channel for delivery of a message may be dynamically determined based on the delivery conditions.

One aspect of the disclosure is method for delivery messages including receiving a plurality of messages for delivery on a network, the messages each containing an identity of at least one recipient. The received messages are parsed to extract message content. Recipient data available to the network is collected by searching for social data, spatial data, temporal data and logical data relating to the message recipients and the extracted message content. The messages are analyzed to identify relationships between the extracted message content and the message recipients. The messages are grouped into a cluster based upon the identified relationships and a rule set, the cluster comprising messages that satisfy rules of the rule set. The cluster is analyzed to determine a cluster label for identification of the cluster to the message recipients.

Another aspect of the disclosure is a computer-readable medium encoding instructions for performing a method for delivery of messages. The method includes including receiving a plurality of messages for delivery on a network, the messages each containing an identity of at least one recipient. The received messages are parsed to extract message content. Recipient data available to the network is collected by searching for social data, spatial data, temporal data and logical data relating to the message recipients and the extracted message content. The messages are analyzed to identify relationships between the extracted message content and the message recipients. The messages are grouped into a cluster based upon the identified relationships and a rule set, the cluster comprising messages that satisfy rules of the rule set. The cluster is analyzed to determine a cluster label for identification of the cluster to the message recipients.

In yet another aspect, the disclosure describes a system that includes a plurality of processors where an attention engine is implemented on one of the plurality of processors for receiving a plurality of messages for at least one recipient via a network, wherein the messages contain an identity of at least one recipient. An attribution engine is implemented on one of the plurality of processors for extracting message content from the received messages. A message intake manager is implemented on one of the plurality of processors for collecting recipient data available to the network by searching for social data, spatial data, temporal data and logical data relating to the at least one recipient and the extracted message content. A correlation engine is implemented on one of the plurality of processors for correlating relationships between the extracted message content and the at least one recipient associated with the messages. A cluster building engine is implemented on one of the plurality of processors for forming clusters via grouping the messages based upon the correlated relationships between the extracted message content and the at least one recipient. A summarization engine is implemented on one of the plurality of processors for summarizing the content of the messages contained within each of the clusters formed by the cluster building engine. The system further includes a labeling engine implemented on one of the plurality of processors for analyzing the summarized cluster content produced by the summarization engine to determine a label, wherein the label provides a visual cluster summarization representation for the at least one recipient of the clusters.

These and various other features as well as advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. Additional features are set forth in the description that follows and, in part, will be apparent from the description, or may be learned by practice of the described embodiments. The benefits and features will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application, are illustrative of embodiments systems and methods described below and are not meant to limit the scope of the disclosure in any manner, which scope shall be based on the claims appended hereto.

FIG. 1 illustrates an example of the relationships between RWEs and IOs on the W4 COMN.

FIG. 2 illustrates an example of metadata defining the relationships between RWEs and IOs on the W4 COMN.

FIG. 3 illustrates a conceptual model of the W4 COMN.

FIG. 4 illustrates the functional layers of the W4 COMN architecture.

FIG. 5 illustrates an embodiment of analysis components of a W4 engine as shown in FIG. 2.

FIG. 6 illustrates an embodiment of a W4 engine showing different components within the sub-engines described generally above with reference to FIG. 5.

FIGS. 7A-C illustrate elements of embodiments of a W4 engine adapted to perform W4 message and cluster delivery as described herein.

FIG. 8 illustrates an embodiment of a method for delivering messages over a network based on social, temporal, spatial and topical data for entities on the network.

FIG. 9 illustrates a flowchart for an embodiment of augmented messaging.

FIG. 9A illustrates a flowchart for an alternate embodiment.

FIGS. 10A-B illustrate other embodiments of a system for delivering augmented messages over a network.

FIG. 11 depicts non-limiting examples of augmented messages sent from a sender to three different recipients.

FIG. 12 illustrates a flowchart for an embodiment for creating message clusters.

FIG. 13 illustrates a flowchart for an embodiment for detection actions based on t message clusters created, with reference to FIG. 12.

FIGS. 14A-C depict a non-limiting example of message cluster related to an event.

DETAILED DESCRIPTION

This disclosure describes a communication network, referred herein as the “W4 Communications Network” or W4 COMN, that uses information related to the “Who, What, When and Where” of interactions with the network to provide improved services to the network's users. The W4 COMN is a collection of users, devices and processes that foster both synchronous and asynchronous communications between users and their proxies. It includes an instrumented network of sensors providing data recognition and collection in real-world environments about any subject, location, user or combination thereof.

As a communication network, the W4 COMN handles the routing/addressing, scheduling, filtering, prioritization, replying, forwarding, storing, deleting, privacy, transacting, triggering of a new message, propagating changes, transcoding and linking. Furthermore, these actions can be performed on any communication channel accessible by the W4 COMN.

The W4 COMN uses a data modeling strategy for creating profiles for not only users and locations but also any device on the network and any kind of user-defined data with user-specified conditions from a rich set of possibilities. Using Social, Spatial, Temporal and Logical data available about a specific user, topic or logical data object, every entity known to the W4 COMN can be mapped and represented against all other known entities and data objects in order to create both a micro graph for every entity as well as a global graph that interrelates all known entities against each other and their attributed relations.

In order to describe the operation of the W4 COMN, two elements upon which the W4 COMN is built must first be introduced, real-world entities and information objects. These distinctions are made in order to enable correlations to be made from which relationships between electronic/logical objects and real objects can be determined. A real-world entity (RWE) refers to a person, device, location, or other physical thing known to the W4 COMN. Each RWE known to the W4 COMN is assigned or otherwise provided with a unique W4 identification number that absolutely identifies the RWE within the W4 COMN.

RWEs may interact with the network directly or through proxies, which may themselves be RWEs. Examples of RWEs that interact directly with the W4 COMN include any device such as a sensor, motor, or other piece of hardware that connects to the W4 COMN in order to receive or transmit data or control signals. Because the W4 COMN can be adapted to use any and all types of data communication, the devices that may be RWEs include all devices that can serve as network nodes or generate, request and/or consume data in a networked environment or that can be controlled via the network. Such devices include any kind of “dumb” device purpose-designed to interact with a network (e.g., cell phones, cable television set top boxes, fax machines, telephones, and radio frequency identification (RFID) tags, sensors, etc.). Typically, such devices are primarily hardware and their operations can not be considered separately from the physical device.

Examples of RWEs that must use proxies to interact with W4 COMN network include all non-electronic entities including physical entities, such as people, locations (e.g., states, cities, houses, buildings, airports, roads, etc.) and things (e.g., animals, pets, livestock, gardens, physical objects, cars, airplanes, works of art, etc.), and intangible entities such as business entities, legal entities, groups of people or sports teams. In addition, “smart” devices (e.g., computing devices such as smart phones, smart set top boxes, smart cars that support communication with other devices or networks, laptop computers, personal computers, server computers, satellites, etc.) are also considered RWEs that must use proxies to interact with the network. Smart devices are electronic devices that can execute software via an internal processor in order to interact with a network. For smart devices, it is actually the executing software application(s) that interact with the W4 COMN and serve as the devices' proxies.

The W4 COMN allows associations between RWEs to be determined and tracked. For example, a given user (an RWE) may be associated with any number and type of other RWEs including other people, cell phones, smart credit cards, personal data assistants, email and other communication service accounts, networked computers, smart appliances, set top boxes and receivers for cable television and other media services, and any other networked device. This association may be made explicitly by the user, such as when the RWE is installed into the W4 COMN. An example of this is the set up of a new cell phone, cable television service or email account in which a user explicitly identifies an RWE (e.g., the user's phone for the cell phone service, the user's set top box and/or a location for cable service, or a username and password for the online service) as being directly associated with the user. This explicit association may include the user identifying a specific relationship between the user and the RWE (e.g., this is my device, this is my home appliance, this person is my friend/father/son/etc., this device is shared between me and other users, etc.). RWEs may also be implicitly associated with a user based on a current situation. For example, a weather sensor on the W4 COMN may be implicitly associated with a user based on information indicating that the user lives or is passing near the sensor's location.

An information object (IO), on the other hand, is a logical object that stores, maintains, generates, serves as a source for or otherwise provides data for use by RWEs and/or the W4 COMN. IOs are distinct from RWEs in that IOs represent data, whereas RWEs may create or consume data (often by creating or consuming IOs) during their interaction with the W4 COMN. Examples of IOs include passive objects such as communication signals (e.g., digital and analog telephone signals, streaming media and interprocess communications), email messages, transaction records, virtual cards, event records (e.g., a data file identifying a time, possibly in combination with one or more RWEs such as users and locations, that may further be associated with a known topic/activity/significance such as a concert, rally, meeting, sporting event, etc.), recordings of phone calls, calendar entries, web pages, database entries, electronic media objects (e.g., media files containing songs, videos, pictures, images, audio messages, phone calls, etc.), electronic files and associated metadata.

In addition, IOs include any executing process or application that consumes or generates data such as an email communication application (such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaring application, a word processing application, an image editing application, a media player application, a weather monitoring application, a browser application and a web page server application. Such active IOs may or may not serve as a proxy for one or more RWEs. For example, voice communication software on a smart phone may serve as the proxy for both the smart phone and for the owner of the smart phone.

An IO in the W4 COMN may be provided a unique W4 identification number that absolutely identifies the IO within the W4 COMN. Although data in an IO may be revised by the act of an RWE, the IO remains a passive, logical data representation or data source and, thus, is not an RWE.

For every IO there are at least three classes of associated RWEs. The first is the RWE who owns or controls the IO, whether as the creator or a rights holder (e.g., an RWE with editing rights or use rights to the IO). The second is the RWE(s) that the IO relates to, for example by containing information about the RWE or that identifies the RWE. The third are any RWEs who then pay any attention (directly or through a proxy process) to the IO, in which “paying attention” refers to accessing the IO in order to obtain data from the TO for some purpose.

“Available data” and “W4 data” means data that exists in an IO in some form somewhere or data that can be collected as needed from a known IO or RWE such as a deployed sensor. “Sensor” means any source of W4 data including PCs, phones, portable PCs or other wireless devices, household devices, cars, appliances, security scanners, video surveillance, RFID tags in clothes, products and locations, online data or any other source of information about a real-world user/topic/thing (RWE) or logic-based agent/process/topic/thing (IO).

FIG. 1 illustrates an example of the relationships between RWEs and IOs on the W4 COMN. In the embodiment illustrated, a user 102 is a RWE of the network provided with a unique network ID. The user 102 is a human that communicates with the network via the proxy devices 104, 106, 108, 110 associated with the user 102, all of which are RWEs of the network and provided with their own unique network ID. Some of these proxies may communicate directly with the W4 COMN or may communicate with the W4 COMN via IOs such as applications executed on or by the device.

As mentioned above the proxy devices 104, 106, 108, 110 may be explicitly associated with the user 102. For example, one device 104 may be a smart phone connected by a cellular service provider to the network and another device 106 may be a smart vehicle that is connected to the network. Other devices may be implicitly associated with the user 102. For example, one device 108 may be a “dumb” weather sensor at a location matching the current location of the user's cell phone 104, and thus implicitly associated with the user 102 while the two RWEs 104, 108 are co-located. Another implicitly associated device 110 may be a sensor 110 for physical location 112 known to the W4 COMN. The location 112 is known, either explicitly (through a user-designated relationship, e.g., this is my home, place of employment, parent, etc.) or implicitly (the user 102 is often co-located with the RWE 112 as evidenced by data from the sensor 110 at that location 112), to be associated with the first user 102.

The user 102 may also be directly associated with other people, such as the person 140 shown, and then indirectly associated with other people 142, 144 through their associations as shown. Again, such associations may be explicit (e.g., the user 102 may have identified the associated person 140 as his/her father, or may have identified the person 140 as a member of the user's social network) or implicit (e.g., they share the same address).

Tracking the associations between people (and other RWEs as well) allows the creation of the concept of “intimacy”: Intimacy being a measure of the degree of association between two people or RWEs. For example, each degree of removal between RWEs may be considered a lower level of intimacy, and assigned lower intimacy score. Intimacy may be based solely on explicit social data or may be expanded to include all W4 data including spatial data and temporal data.

Each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144 of the W4 COMN may be associated with one or more IOs as shown. Continuing the examples discussed above, FIG. 1 illustrates two IOs 122, 124 as associated with the cell phone device 104. One IO 122 may be a passive data object such as an event record that is used by scheduling/calendaring software on the cell phone, a contact IO used by an address book application, a historical record of a transaction made using the device 104 or a copy of a message sent from the device 104. The other IO 124 may be an active software process or application that serves as the device's proxy to the W4 COMN by transmitting or receiving data via the W4 COMN. Voice communication software, scheduling/calendaring software, an address book application or a text messaging application are all examples of IOs that may communicate with other IOs and RWEs on the network. The IOs 122, 124 may be locally stored on the device 104 or stored remotely oil some node or datastore accessible to the W4 COMN, such as a message server or cell phone service datacenter. The IO 126 associated with the vehicle 108 may be an electronic file containing the specifications and/or current status of the vehicle 108, such as make, model, identification number, current location, current speed, current condition, current owner, etc. The IO 128 associated with sensor 108 may identify the current state of the subject(s) monitored by the sensor 108, such as current weather or current traffic. The IO 130 associated with the cell phone 110 may be information in a database identifying recent calls or the amount of charges on the current bill.

Furthermore, those RWEs which can only interact with the W4 COMN through proxies, such as the people 102, 140, 142, 144, computing devices 104, 106 and location 112, may have one or more IOs 132, 134, 146, 148, 150 directly associated with them. An example includes IOs 132, 134 that contain contact and other RWE-specific information. For example, a person's IO 132, 146, 148, 150 may be a user profile containing email addresses, telephone numbers, physical addresses, user preferences, identification of devices and other RWEs associated with the user, records of the user's past interactions with other RWE's on the W4 COMN (e.g., transaction records, copies of messages, listings of time and location combinations recording the user's whereabouts in the past), the unique W4 COMN identifier for the location and/or any relationship information (e.g., explicit user-designations of the user's relationships with relatives, employers, co-workers, neighbors, service providers, etc.). Another example of a person's IO 132, 146, 148, 150 includes remote applications through which a person can communicate with the W4 COMN such as an account with a web-based email service such as Yahoo! Mail. The location's IO 134 may contain information such as the exact coordinates of the location, driving directions to the location, a classification of the location (residence, place of business, public, non-public, etc.), information about the services or products that can be obtained at the location, the unique W4 COMN identifier for the location, businesses located at the location, photographs of the location, etc.

In order to correlate RWEs and IOs to identify relationships, the W4 COMN makes extensive use of existing metadata and generates additional metadata where necessary. Metadata is loosely defined as data that describes data. For example, given an IO such as a music File, the core, primary or object data of the music file is the actual music data that is converted by a media player into audio that is heard by the listener. Metadata for the same music file may include data identifying the artist, song, etc., album art, and the format of the music data. This metadata may be stored as part of the music file or in one or more different IOs that are associated with the music file or both. In addition, W4 metadata for the same music file may include the owner of the music file and the rights the owner has in the music file. As another example, if the IO is a picture taken by an electronic camera, the picture may include in addition to the primary image data from which an image may be created on a display, metadata identifying when the picture was taken, where the camera was when the picture was taken, what camera took the picture, who, if anyone, is associated (e.g., designated as the camera's owner) with the camera, and who and what are the subjects of/in the picture. The W4 COMN uses all the available metadata in order to identify implicit and explicit associations between entities and data objects.

FIG. 2 illustrates an example of metadata defining the relationships between RWEs and IOs on the W4 COMN. In the embodiment shown, an IO 202 includes object data 204 and five discrete items of metadata 206, 208, 210, 212, 214. Some items of metadata 208, 210, 212 may contain information related only to the object data 204 and unrelated to any other IO or RWE. For example, a creation date, text or an image that is to be associated with the object data 204 of the IO 202.

Some of items of metadata 206, 214, on the other hand, may identify relationships between the IO 202 and other RWEs and IOs. As illustrated, the IO 202 is associated by one item of metadata 206 with an RWE 220 that RWE 220 is further associated with two IOs 224, 226 and a second RWE 222 based on some information known to the W4 COMN. This part of FIG. 2, for example, could describe the relations between a picture (IO 202) containing metadata 206 that identifies the electronic camera (the first RWE 220) and the user (the second RWE 224) that is known by the system to be the owner of the camera 220. Such ownership information may be determined, for example, from one or another of the IOs 224, 226 associated with the camera 220.

FIG. 2 also illustrates metadata 214 that associates the IO 202 with another IO 230. This IO 230 is itself associated with three other IOs 232, 234, 236 that are further associated with different RWEs 242, 244, 246. This part of FIG. 2, for example, could describe the relations between a music file (IO 202) containing metadata 206 that identifies the digital rights file (the first IO 230) that defines the scope of the rights of use associated with this music file 202. The other IOs 232, 234, 236 are other music files that are associated with the rights of use and which are currently associated with specific owners (RWEs 242, 244, 246).

FIG. 3 illustrates a conceptual model of the W4 COMN. The W4 COMN 300 creates an instrumented messaging infrastructure in the form of a global logical network cloud conceptually sub-divided into networked-clouds for each of the 4Ws: Who, Where, What and When. In the Who cloud 302 are all users whether acting as senders, receivers, data points or confirmation/certification sources as well as user proxies in the forms of user-program processes, devices, agents, calendars, etc. In the Where cloud 304 are all physical locations, events, sensors or other RWEs associated with a spatial reference point or location. The When cloud 306 is composed of natural temporal events (that is events that are not associated with particular location or person such as days, times, seasons) as well as collective user temporal events (holidays, anniversaries, elections, etc.) and user-defined temporal events (birthdays, smart-timing programs). The What cloud 308 is comprised of all known data—web or private, commercial or user—accessible to the W4 COMN, including for example environmental data like weather and news, RWE-generated data, IOs and IO data, user data, models, processes and applications. Thus, conceptually, most data is contained in the What cloud 308.

As this is just a conceptual model, it should be noted that some entities, sensors or data will naturally exist in multiple clouds either disparate in time or simultaneously. Additionally, some IOs and RWEs may be composites in that they combine elements from one or more clouds. Such composites may be classified or not as appropriate to facilitate the determination of associations between RWEs and IOs. For example, an event consisting of a location and time could be equally classified within the When cloud 306, the What cloud 308 and/or the Where cloud 304.

The W4 engine 310 is center of the W4 COMN's central intelligence for making all decisions in the W4 COMN. An “engine” as referred to herein is meant to describe a software, hardware or firmware (or combinations thereof) system, process or functionality that performs or facilitates the processes, features and/or functions described herein (with or without human interaction or augmentation). The W4 engine 310 controls all interactions between each layer of the W4 COMN and is responsible for executing any approved user or application objective enabled by W4 COMN operations or interoperating applications. In an embodiment, the W4 COMN is an open platform upon which anyone can write an application. To support this, it includes standard published APIs for requesting (among other things) synchronization, disambiguation, user or topic addressing, access rights, prioritization or other value-based ranking, smart scheduling, automation and topical, social, spatial or temporal alerts.

One function of the W4 COMN is to collect data concerning all communications and interactions conducted via the W4 COMN, which may include storing copies of IOs and information identifying all RWEs and other information related to the IOs (e.g., who, what, when, where information). Other data collected by the W4 COMN may include information about the status of any given RWE and IO at any given time, such as the location, operational state, monitored conditions (e.g., for an RWE that is a weather sensor, the current weather conditions being monitored or for an RWE that is a cell phone, its current location based on the cellular towers it is in contact with) and current status.

The W4 engine 310 is also responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN. The function of identifying RWEs associated with or implicated by IOs and actions performed by other RWEs is referred to as entity extraction. Entity extraction includes both simple actions, such as identifying the sender and receivers of a particular IO, and more complicated analyses of the data collected by and/or available to the W4 COMN, for example determining that a message listed the time and location of an upcoming event and associating that event with the sender and receiver(s) of the message based on the context of the message or determining that an RWE is stuck in a traffic jam based on a correlation of the RWE's location with the status of a co-located traffic monitor.

It should be noted that when performing entity extraction from an IO, the IO can be an opaque object with only W4 metadata related to the object (e.g., date of creation, owner, recipient, transmitting and receiving RWEs, type of IO, etc.), but no knowledge of the internals of the IO (i.e., the actual primary or object data contained within the object). Knowing the content of the IO does not prevent W4 data about the IO (or RWE) to be gathered. The content of the IO if known can also be used in entity extraction, if available, but regardless of the data available entity extraction is performed by the network based on the available data. Likewise, W4 data extracted around the object can be used to imply attributes about the object itself, while in other embodiments, full access to the IO is possible and RWEs can thus also be extracted by analyzing the content of the object, e.g. strings within an email are extracted and associated as RWEs to for use in determining the relationships between the sender, user, topic or other RWE or IO impacted by the object or process.

In an embodiment, the W4 engine 310 represents a group of applications executing on one or more computing devices that are nodes of the W4 COMN. For the purposes of this disclosure, a computing device is a device that includes a processor and memory for storing data and executing software (e.g., applications) that perform the functions described. Computing devices may be provided with operating systems that allow the execution of software applications in order to manipulate data.

In the embodiment shown, the W4 engine 310 may be one or a group of distributed computing devices, such as a general-purpose personal computers (PCs) or purpose built server computers, connected to the W4 COMN by suitable communication hardware and/or software. Such computing devices may be a single device or a group of devices acting together. Computing devices may be provided with any number of program modules and data files stored in a local or remote mass storage device and local memory (e.g., RAM) of the computing device. For example, as mentioned above, a computing device may include an operating system suitable for controlling the operation of a networked computer, such as the WINDOWS XP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.

Some RWEs may also be computing devices such as smart phones, web-enabled appliances, PCs, laptop computers, and personal data assistants (PDAs). Computing devices may be connected to one or more communications networks such as the Internet, a publicly switched telephone network, a cellular telephone network, a satellite communication network, a wired communication network such as a cable television or private area network. Computing devices may be connected any such network via a wired data connection or wireless connection such as a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephone connection.

Local data structures, including discrete IOs, may be stored on a mass storage device (not shown) that is connected to, or part of, any of the computing devices described herein including the W4 engine 310. For example, in an embodiment, the data backbone of the W4 COMN, discussed below, includes multiple mass storage devices that maintain the IOs, metadata and data necessary to determine relationships between RWEs and IOs as described herein. A mass storage device includes some form of computer-readable media and provides non-volatile storage of data and software for retrieval and later use by one or more computing devices. Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by a computing device.

By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

FIG. 4 illustrates the functional layers of the W4 COMN architecture. At the lowest layer, referred to as the sensor layer 402, is the network 404 of the actual devices, users, nodes and other RWEs. The instrumentation of the network nodes to utilize them as sensors include known technologies like web analytics, GPS, cell-tower pings, use logs, credit card transactions, online purchases, explicit user profiles and implicit user profiling achieved through behavioral targeting, search analysis and other analytics models used to optimize specific network applications or functions.

The next layer is the data layer 406 in which the data produced by the sensor layer 402 is stored and cataloged. The data may be managed by either the network 404 of sensors or the network infrastructure 406 that is built on top of the instrumented network of users, devices, agents, locations, processes and sensors. The network infrastructure 408 is the core under-the-covers network infrastructure that includes the hardware and software necessary to receive that transmit data from the sensors, devices, etc. of the network 404. It further includes the processing and storage capability necessary to meaningfully categorize and track the data created by the network 404.

The next layer of the W4 COMN is the user profiling layer 410. This layer 410 may further be distributed between the network infrastructure 408 and user applications/processes 412 executing on the W4 engine or disparate user computing devices. In the user profiling layer 410 that functions as W4 COMN's user profiling layer 410. Personalization is enabled across any single or combination of communication channels and modes including email, IM, texting (SMS, etc.), photobloging, audio (e.g. telephone call), video (teleconferencing, live broadcast), games, data confidence processes, security, certification or any other W4 COMN process call for available data.

In one embodiment, the user profiling layer 410 is a logic-based layer above all sensors to which sensor data are sent in the rawest form to be mapped and placed into the W4 COMN data backbone 420. The data (collected and refined, related and deduplicated, synchronized and disambiguated) are then stored in one or a collection of related databases available to all processes of all applications approved on the W4 COMN. All Network-originating actions and communications are based upon the fields of the data backbone, and some of these actions are such that they themselves become records somewhere in the backbone, e.g. invoicing, while others, e.g. fraud detection, synchronization, disambiguation, can be done without an impact to profiles and models within the backbone.

Actions originating from anything other than the network, e.g., RWEs such as users, locations, proxies and processes, come from the applications layer 414 of the W4 COMN. Some applications may be developed by the W4 COMN operator and appear to be implemented as part of the communications infrastructure 408, e.g. email or calendar programs because of how closely the operate with the sensor processing and user profiling layer 410. The applications 412 also serve some role as a sensor in that they, through their actions, generate data back to the data layer 406 via the data backbone concerning any data created or available due to the applications execution.

The applications layer 414 also provides a personalized user interface (UI) based upon device, network, carrier as well as user-selected or security-based customizations. Any UI can operate within the W4 COMN if it is instrumented to provide data on user interactions or actions back to the network. This is a basic sensor function of any W4 COMN application/UI, and although the W4 COMN can interoperate with applications/UIs that are not instrumented, it is only in a delivery capacity and those applications/UIs would not be able to provide any data (let alone the rich data otherwise available from W4-enabled devices.)

In the case of W4 COMN mobile devices, the UI can also be used to confirm or disambiguate incomplete W4 data in real-time, as well as correlation, triangulation and synchronization sensors for other nearby enabled or non-enabled devices. At some point, the network effects of enough enabled devices allow the network to gather complete or nearly complete data (sufficient for profiling and tracking) of a non-enabled device because of it's regular intersection and sensing by enabled devices in it's real-world location.

Above the applications layer 414 (and sometimes hosted within it) is the communications delivery network(s) 416. This can be operated by the W4 COMN operator or be independent third-party carrier service, but in either case it functions to deliver the data via synchronous or asynchronous communication. In every case, the communication delivery network 414 will be sending or receiving data (e.g., http or IP packets) on behalf of a specific application or network infrastructure 408 request.

The communication delivery layer 418 also has elements that act as sensors including W4 entity extraction from telephone calls, emails, blogs, etc. as well as specific user commands within the delivery network context, e.g., “save and prioritize this call” said before end of call may trigger a recording of the previous conversation to be saved and for the W4 entities within the conversation to analyzed and increased in weighting prioritization decisions in the personalization/user profiling layer 410.

FIG. 5 illustrates an embodiment of analysis components of a W4 engine as shown in FIG. 3. As discussed above, the W4 Engine is responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN.

In one embodiment the W4 engine connects, interoperates and instruments all network participants through a series of sub-engines that perform different operations in the entity extraction process. One such sub-engine is an attribution engine 504. The attribution engine 504 tracks the real-world ownership, control, publishing or other conditional rights of any RWE in any IO. Whenever a new IO is detected by the W4 engine 502, e.g., through creation or transmission of a new message, a new transaction record, a new image file, etc., ownership is assigned to the IO. The attribution engine 504 creates this ownership information and further allows this information to be determined for each IO known to the W4 COMN.

The W4 engine 502 further includes a correlation engine 506. The correlation engine 506 operates in two capacities: first, to identify associated RWEs and IOs and their relationships (such as by creating a combined graph of any combination of RWEs and IOs and their attributes, relationships and reputations within contexts or situations) and second, as a sensor analytics pre-processor for attention events from any internal or external source.

In one embodiment, the identification of associated RWEs and IOs function of the correlation engine 506 is done by graphing the available data. In this embodiment, a histogram of all RWEs and IOs is created, from which correlations based on the graph may be made. Graphing, or the act of creating a histogram, is a computer science method of identify a distribution of data in order to identify relevant information and make correlations between the data. In a more general mathematical sense, a histogram is simply a mapping m_(i), that counts the number of observations that fall into various disjoint categories (known as bins), whereas the graph of a histogram is merely one way to represent a histogram. By selecting each IO, RWE, and other known parameters (e.g., times, dates, locations, etc.) as different bins and mapping the available data, relationships between RWEs, IOs and the other parameters can be identified.

In an embodiment, the W4 data are processed and analyzed using data models that treat data not as abstract signals stored in databases, but rather as IOs that represent RWEs that actually exist, have existed, or will exist in real space, real time, and are real people, objects, places, times, and/or events. As such, the data model for W4 IOs that represent W4 RWEs (Where/When/Who/What) will model not only the signals recorded from the RWEs or about the RWEs, but also represent these RWEs and their interactions in ways that model the affordances and constraints of entities and activities in the physical world. A notable aspect is the modeling of data about RWEs as embodied and situated in real world contexts so that the computation of similarity, clustering, distance, and inference take into account the states and actions of RWEs in the real world and the contexts and patterns of these states and actions.

For example, for temporal data the computation of temporal distance and similarity in a W4 data model cannot merely treat time as a linear function. The temporal distance and similarity between two times is dependent not only on the absolute linear temporal delta between them (e.g., the number of hours between “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 20, 7:00 pm Pacific Time”), but even more so is dependent on the context and activities that condition the significance of these times in the physical world and the other W4 RWEs (people, places, objects, and events) etc.) associated with them. For example, in terms of distance and similarity, “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 27, 4:00 pm Pacific Time” may be modeled as closer together in a W4 temporal data model than “Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 20, 7:00 pm Pacific Time” because of the weekly meeting that happens every Tuesday at work at 4:00 pm vs. the dinner at home with family that happens at 7 pm on Tuesdays. Contextual and periodic patterns in time may be important to the modeling of temporal data in a W4 data model.

An even simpler temporal data modeling issue is to model the various periodic patterns of daily life such as day and night (and subperiods within them such as morning, noon, afternoon, evening, etc.) and the distinction between the workweek and the weekend. In addition, salient periods such as seasons of the year and salient events such as holidays also affect the modeling of temporal data to determine similarity and distance. Furthermore, the modeling of temporal data for IOs that represent RWEs should correlate temporal, spatial, and weather data to account for the physical condition of times at different points on the planet. Different latitudes have different amounts of daylight and even are opposite between the northern and southern hemispheres. Similar contextual and structural data modeling issues arise in modeling data from and about the RWEs for people, groups of people, objects, places, and events.

With appropriate data models for IOs that represent data from or about RWEs, a variety of machine learning techniques can be applied to analyze the W4 data. In an embodiment, W4 data may modeled as a “feature vector” in which the vector includes not only raw sensed data from or about W4 RWEs, but also higher order features that account for the contextual and periodic patterns of the states and action of W4 RWEs. Each of these features in the feature vector may have a numeric or symbolic value that can be compared for similarity to other numeric or symbolic values in a feature space. Each feature may also be modeled with an additional value from 0 to 1 (a certainty value) to represent the probability that the feature is true. By modeling W4 data about RWEs in ways that account for the affordances and constraints of their context and patterns in the physical world in features and higher order features with or without certainty values, this data (whether represented in feature vectors or by other data modeling techniques) can then be processed to determine similarity, difference, clustering, hierarchical and graph relationships, as well as inferential relationships among the features and feature vectors.

A wide variety of statistical and machine learning techniques can be applied to W4 data from simple histograms to Sparse Factor Analysis (SFA), Hidden Markov Models (I-IMMs), Support Vector Machines (SVMs), Bayesian Methods, etc. Such learning algorithms may be populated with data models that contain features and higher order features represent not just the “content” of the signals stored as IOs, e.g., the raw W4 data, but also model the contexts and patterns of the RWEs that exist, have existed, or will exist in the physical world from which these data have been captured.

As a pre-processor, the correlation engine 506 monitors the information provided by RWEs in order to determine if any conditions are identified that may trigger an action on the part of the W4 engine 502. For example, if a delivery condition has be associated with a message, when the correlation engine 506 determines that the condition is met, it can transmit the appropriate trigger information to the W4 engine 502 that triggers delivery of the message.

The attention engine 508 instruments all appropriate network nodes, clouds, users, applications or any combination thereof and includes close interaction with both the correlation engine 506 and the attribution engine 504.

FIG. 6 illustrates an embodiment of a W4 engine showing different components within the sub-engines described generally above with reference to FIG. 4. In one embodiment the W4 engine 600 includes an attention engine 608, attribution engine 604 and correlation engine 606 with several sub-managers based upon basic function.

The attention engine 608 includes a message intake and generation manager 610 as well as a message delivery manager 612 that work closely with both a message matching manager 614 and a real-time communications manager 616 to deliver and instrument all communications across the W4 COMN.

The attribution engine 604 works within the user profile manager 618 and in conjunction with all other modules to identify, process/verify and represent ownership and rights information related to RWEs, IOs and combinations thereof.

The correlation engine 606 dumps data from both of its channels (sensors and processes) into the same data backbone 620 which is organized and controlled by the W4 analytics manager 622 and includes both aggregated and individualized archived versions of data from all network operations including user logs 624, attention rank place logs 626, web indices and environmental logs 618, e-commerce and financial transaction information 630, search indexes and logs 632, sponsor content or conditionals, ad copy and any and all other data used in any W4 COMN process, IO or event. Because of the amount of data that the W4 COMN will potentially store, the data backbone 620 includes numerous database servers and datastores in communication with the W4 COMN to provide sufficient storage capacity.

As discussed above, the data collected by the W4 COMN includes spatial data, temporal data, RWE interaction data, IO content data (e.g., media data), and user data including explicitly-provided and deduced social and relationship data. Spatial data may be any data identifying a location associated with an RWE. For example, the spatial data may include any passively collected location data, such as cell tower data, global packet radio service (GPRS) data, global positioning service (GPS) data, WI-FI data, personal area network data, IP address data and data from other network access points, or actively collected location data, such as location data entered by the user.

Temporal data is time based data (e.g., time stamps) that relate to specific times and/or events associated with a user and/or the electronic device. For example, the temporal data may be passively collected time data (e.g., time data from a clock resident on the electronic device, or time data from a network clock), or the temporal data may be actively collected time data, such as time data entered by the user of the electronic device (e.g., a user maintained calendar).

The interaction data may be any data associated with user interaction of the electronic device, whether active or passive. Examples of interaction data include interpersonal communication data, media data, relationship data, transactional data and device interaction data, all of which are described in further detail below. Table 1, below, is a non-exhaustive list including examples of electronic data.

TABLE 1 Examples of Electronic Data Spatial Data Temporal Data Interaction Data Cell tower data Time stamps Interpersonal GPRS data Local clock communication data GPS data Network clock Media data WiFi data User input of Relationship data Personal area network data time data Transactional data Network access points data Device interaction data User input of location data Geo-coordinates data

With respect to the interaction data, communications between any RWEs may generate communication data that is transferred via the W4 COMN. For example, the communication data may be any data associated with an incoming or outgoing short message service (SMS) message, email message, voice call (e.g., a cell phone call, a voice over IP call), or other type of interpersonal communication relative to an RWE, such as information regarding who is sending and receiving the communication(s). As described above, communication data may be correlated with, for example, temporal data to deduce information regarding frequency of communications, including concentrated communication patterns, which may indicate user activity information.

Logical and IO data refers to the data contained by an IO as well as data associated with the IO such as creation time, owner, associated RWEs, when the IO was last accessed, etc. If the IO is a media object, the term media data may be used. Media data may include any data relating to presentable media, such as audio data, visual data, and audiovisual data. For example, the audio data may be data relating to downloaded music, such as genre, artist, album and the like, and includes data regarding ringtones, ringbacks, media purchased, playlists, and media shared, to name a few. The visual data may be data relating to images and/or text received by the electronic device (e.g., via the Internet or other network). The visual data may be data relating to images and/or text sent from and/or captured at the electronic device. The audiovisual data may be data associated with any videos captured at, downloaded to, or otherwise associated with the electronic device. The media data includes media presented to the user via a network, such as use of the Internet, and includes data relating to text entered and/or received by the user using the network (e.g., search terms), and interaction with the network media, such as click data (e.g., advertisement banner clicks, bookmarks, click patterns and the like). Thus, the media data may include data relating to the user's RSS feeds, subscriptions, group memberships, game services, alerts, and the like. The media data also includes non-network activity, such as image capture and/or video capture using an electronic device, such as a mobile phone. The image data may include metadata added by the user, or other data associated with the image, such as, with respect to photos, location when the photos were taken, direction of the shot, content of the shot, and time of day, to name a few. As described in further detail below, media data may be used, for example, to deduce activities information or preferences information, such as cultural and/or buying preferences information.

The relationship data may include data relating to the relationships of an RWE or IO to another RWE or IO. For example, the relationship data may include user identity data, such as gender, age, race, name, social security number, photographs and other information associated with the user's identity. User identity information may also include e-mail addresses, login names and passwords. Relationship data may further include data identifying explicitly associated RWEs. For example, relationship data for a cell phone may indicate the user that owns the cell phone and the company that provides the service to the phone. As another example, relationship data for a smart car may identify the owner, a credit card associated with the owner for payment of electronic tolls, those users permitted to drive the car and the service station for the car.

Relationship data may also include social network data. Social network data includes data relating to any relationship that is explicitly defined by a user or other RWE, such as data relating to a user's friends, family, co-workers, business relations, and the like. Social network data nay include, for example, data corresponding with a user-maintained electronic address book. Relationship data may be correlated with, for example, location data to deduce social network information, such as primary relationships (e.g., user-spouse, user-children and user-parent relationships) or other relationships (e.g., user-friends, user-co-worker, user-business associate relationships). Relationship data also may be utilized to deduce, for example, activities information.

The interaction data may also include transactional data. The transactional data may be any data associated with commercial transactions undertaken by or at the mobile electronic device, such as vendor information, financial institution information (e.g., bank information), financial account information (e.g., credit card information), merchandise information and costs/prices information, and purchase frequency information, to name a few. The transactional data may be utilized, for example, to deduce activities and preferences information. The transactional information may also be used to deduce types of devices and/or services the user owns and/or in which the user may have an interest.

The interaction data may also include device or other RWE interaction data. Such data includes both data generated by interactions between a user and a RWE on the W4 COMN and interactions between the RWE and the W4 COMN. RWE interaction data may be any data relating to an RWE's interaction with the electronic device not included in any of the above categories, such as habitual patterns associated with use of an electronic device data of other modules/applications, such as data regarding which applications are used on an electronic device and how often and when those applications are used. As described in further detail below, device interaction data may be correlated with other data to deduce information regarding user activities and patterns associated therewith. Table 2, below, is a non-exhaustive list including examples of interaction data.

TABLE 2 Examples of Interaction Data Type of Data Example(s) Interpersonal Text-based communications, such as SMS communication data and e-mail Audio-based communications, such as voice calls, voice notes, voice mail Media-based communications, such as multimedia messaging service (MMS) communications Unique identifiers associated with a communication, such as phone numbers, e- mail addresses, and network addresses Media data Audio data, such as music data (artist, genre, track, album, etc.) Visual data, such as any text, images and video data, including Internet data, picture data, podcast data and playlist data Network interaction data, such as click patterns and channel viewing patterns Relationship data User identifying information, such as name, age, gender, race, and social security number Social network data Transactional data Vendors Financial accounts, such as credit cards and banks data Type of merchandise/services purchased Cost of purchases Inventory of purchases Device interaction data Any data not captured above dealing with user interaction of the device, such as patterns of use of the device, applications utilized, and so forth

Augmented Delivery of Messages on the W4 COMN or Other Networks

One notable aspect of the W4 COMN is the ability to use W4 data to allow users to tailor when and how messages are delivered to other users or their proxies. The information obtained about a W4 entity from any source or communication channel may be used as a basis for delivery conditions for any message delivered via the W4 COMN on any communication channel interoperating with the W4 COMN.

The delivery of messages is a network personal information management (PIM) operation that allows both explicit and implicit automation of W4 COMN circuits, processes and events by logic-based conditions including means for senders, receivers and delivery conditions to be expressed, weighted and prioritized in W4 analytical processes for testing delivery conditions or network environmental conditions. W4 message delivery includes user-, process- or system-generated messages targeted to an intersection of any topical, spatial, temporal, and/or social variables.

To continue the “Who, What, When, Where” conceptualization discussed above, W4 message delivery allows messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. Table 3, below, provides a matrix of some examples of different “Who, What, When, Where” combinations that could be used in W4 message delivery. The listings in Table 3 are not complete nor exhaustive, but are provided to give an idea of the plethora of different message delivery options provided by the W4 COMN.

TABLE 3 Examples of W4 Message Delivery Delivery From Conditions To Explanation Example Message Who When Who Person to Person based on time Happy Birthday! (standard phone or email) or future scheduled (send to daughter on birthday) Who Who Who Person to Person based on Social Stay Away from that Conditions (messages between Boy.; Don't talk to people based on whether other strangers. people are present or not or detection of a social relationship) Who Where Who Person to Person based on Don't forget to buy location (send shopping list when milk. roommate enters store) Who What Who Person to Person based on This movie is too scary state/topic (raining reminder, for you.; It will freeze content monitor, surf conditions, tonight-drain the stock monitor) sprinklers. Where When Who Location to Person based on time Don't forget your wife's (special lunch deals now, birthday at Macy's. birthday alert reminder, costa rica invites you to warmer weather, mardi gras) Where Who Who Location to Person based on Bring your friend back Person (new ad format, lovers to Jamaica. enjoy Italian food at Joe's) Where Where Who Location to Person based Don't buy Starbucks, Location (comparative/poaching come to coffee people.; advertising, sub-area specialized Welcome to Boston eat calls at conference) here. Where What Who Location to Person based on Umbrellas available in State/Topic (personalized offers the lobby. from venue based on profile, personalized coupons) When When Who Time to Person based on time It's summertime don't (calendar updates user, seasonal forget your sunblock.; x reminders) shopping days to Christmas When Who Who Time to Person based on Person Ask Sally what she (holiday messages to spouse wants for your when partner is near) anniversary. When Where Who Time to Person based on Don't forget to come to Location (event messages patron; the Pride Parade.; It's Macy's is closing at 9:00 pm; Happy Hour at Joe's. rush hour re-route) When What Who Time to Person based on Stop reading email and State/Topic (can be based on a play with your kids. state of a variable such as rain sensor or on the output of a process) What When Who State/Topic to Person based on Car says change your Time (user-defined or process- oil.; Fridge wants more generated message) beer.; Your hair wants to be cut. What Who Who State/Topic to Person based on Diamonds are forever Person delivered when you meet wife for dinner.; Better safe than sorry, use mouthwash. What Where Who State/Topic to Person based on Don't' drink and drive.; Location (public service Don't' forget to buy announcement) cheese.; Construction scheduled for next week, use alternate route. What What Who State/Topic to Person based on Your dreams want you State/Topic (medical advertising) back.; Losing your hair. Who When Where Person to Location based on Happy New Year.; Call Time a cab as bar closes.; Did Suzie show up for ballet class? Who Who Where Person to Location based on Don't let Sam drink.; Person (restraining order Jane is authorized to enforcement, messages to pick up my child.; This businesses, schools, etc.) patron is not of legal age. Who Where Where Person to Location based on Make me a reservation Location at Joe's when I arrive in NYC.; Get my room ready. Who What Where Person to Location based on Diabetic alert to State/Topic hospital.; Save me the nightly special.; Stop Suzie from using library computer to look at porn. Where When Where Location to Location based on Flight 85 is delayed.; Time Kids on the way walking home (early dismissal). Where Who Where Location to Location based on Elite customer is being Person sent over, please treat well.; Suzie has arrived safely at after school. Where Where Where Location to Location based on Conference room Location contacts lobby because meeting is in new location. Where What Where Location to Location based on Airport updates the State/Topic (automatic inventory hotel that I'm stuck in restock order) traffic in a cab.; Full hotel looks for rooms at close by. When When Where Time to Location based on Time Weekly meeting contacts conference room of time change.; Thanksgiving reminds Safeway only two days to holiday. When Who Where Time to Location based on Lent sends message to Person restaurant to only serve fish.; 4:00 pm contacts school to confirm arrival.; Elvis the left the building. When Where Where Time to Location based on Weekly meeting Location contacts conference room because attendees are in Japan When What Where Time to Location based on Wedding to reception State/Topic hall that its raining/xtra attendees. What When Where State/Topic to Location based on Car to vendor(s) for oil Time change. What Who Where State/Topic to Location based on Diet to restaurant for Person special meal requirements. What Where Where State/Topic to Location based on Education plan to Location bookstore when child enters the mall.; Car to tow truck venue(s) with car location. What What Where State/Topic to Location based on Refrigerator to Safeway State/Topic for more eggs. Who When When Person to Time based on Time One week before my birthday, broadcast.; schedule reminder for annual physical. Who Who When Person to Time based on Person Get STD test next week after dating that person. Who Where When Person to Time based on Schedule dinner with Location Marc when he is in town. Who What When Person to Time based on Send a message to the State/Topic future to renew warranty. Where When When Place to Time based on Time Gallery sends reminders based upon a opening night, Macy's sends out “One week to the Men's sale.” Where Who When Place to Time based on Person Cody's books sends to calendar that Al Gore is speaking. Where Where When Place to Time based on Location Restaurant sends out a message in a given radius “we are closing in an hour and still have tables.” Where What When Place to Time based on Vendor contacts loyal State/Topic customer after six weeks of no show. When When When Time to Time based on Time Calendar to calendar based on missed meeting. When Who When Time to Time based on Person Calendar to calendar based on required attendee's absence. When Where When Time to Time based on Location Calendar to Calendar based on room availability. When What When Time to Time based on Calendar to Calendar State/Topic based on subject matter of meeting.; AC filter replacement. What When When State/Topic to Time based on AC to calendar based on Time service cycle.; Diet plan to calendar to schedule exercise due to lack thereof. What Who When State/Topic to Time based on Event Plan to lunchtime Person for vegetarian based on Marc joining.; collaborative restaurant recommendation. What Where When State/Topic to Time based on Put your chains on Location (reminders to buy things before going over based on when/where you are; Grant's pass; product to calendar with Strawberries should be availability for locations) ripe next week. What What When State/Topic to Time based on Refrigerator to Sunday State/Topic that milk is expired.; Car to weekend-wash. Who When When Person to Time based on Time Schedule annual/recurring maintenance.; Who Who When Person to Time based on Person Auto birthday reminder based on social interaction.; Parent schedules discussion with kid based on associations. Who Where When Person to Time based on Make follow up Location (travel reminder to leave appointments.; Wake up in time to reach destination on message to spouse when time; auto refresh on calendar to traveling.; Closing push out rest of day by x time) notice triggered by proximity. Who What When Person to Time based on Santa to Christmas that State/Topic (schedule future Johnny is Bad.; cancel event based on past due now; dinner because I'm schedule discussion on online sick.; schedule gym browsing with child based on surf time because I ate too history) much.; Where When When Place to Time based on Time Meeting room to calendar that current meeting is going long.; School to calendar that students are getting out early. Where Who When Place to Time based on Person Venue to calendars that (Tuesday kids eat free; Wed is celebrity ate here; theme ladies night) park to calendar for summer camp based on visit with child Where Where When Place to Time based on Location SFO to calendar that plane is still in Denver; School to calendar based on museum visit. Where What When Place to Time based on Venue to calendar to State/Topic leave early due to traffic/terrorist threat.; farm to market calendar of expected harvest When When What Time to State/Topic based on Calendar to Car based Time on 3 months since last oil change.; calendar to alarm clock/coffee maker. When Who What Time to State/Topic based on Calendar to Refrigerator Person based on Uncle Joe staying with us. When Where What Time to State/Topic based on Calendar to bathtub Location based on being at gym. When What What Time to State/Topic based on Birthday to car based on State/Topic web browsing.; sunset to car to make sure the door's are locked. What When What State/Topic to State/Topic based Alarm clock to washing on Time machine/coffee machine with start time. What Who What State/Topic to State/Topic based Car to clothes because on Person of association.; Dating plan to clothes not to be worn around sally.; Dating plan to toothbrush to be used and brought. What Where What State/Topic to State/Topic based 12 steps to car based on on Location entering bar.; Weight to belt based on restaurant.; Badge beeps car that it has been forgotten. What What What State/Topic to State/Topic based Shirt to washing on State/Topic machine to remove black sock from whites.; Forgot item to backpack. Who When What Person to State/Topic based on Programming alarm Time clock.; Attorney registering with bar association.; Tell the bath to start at 6:00 pm Who Who What Person to State/Topic based on Parent to child's Person clothes.; Adaptive fashion based on association.; Bumper stickers on car change based on who is watching. Who Where What Person to State/Topic based on Customized content Location (user to user-defined instructions depending plan based on attention) on locale.; Auto- changing location sensitive ringtones. Who What What Person to State/Topic based on Wife to marriage plan State/Topic (user to user-defined based on husband's plan based on feedback; parent to surfing.; Message to car phone if drugs are present- to beep when less than behavior modification space) half gas.; Where When What Place to State/Topic based on Dealership to car to Time come in for tune up. Where Who What Place to State/Topic based on Restaurant to car based Person on occupants.; Playground to parenting plan based on play. Where Where What Place to State/Topic based on Parking lot to car with Location closest vacancies/best prices. Where What What Place to State/Topic based on Repair vendor to car State/Topic after accident.

The list provided in Table 3 is a very limited list of the possibilities for message delivery via the W4 COMN. It should be noted that the delivery conditions could be a simple condition, such as a time or detection that a designated RWE is at a location, or a more complex condition based on the occurrence of multiple conditions, either at the same time or in a specific order, such as deliver only on the day of a baseball game to a RWE near a specified location. In the baseball game example, the baseball game may be considered to be an event, with its own unique W4 identifier, that is associated with a location and a time period. For events such as sporting events, meetings, holidays, etc., one or more IOs may exist on the W4 COMN or an electronic calendar that are a proxy for the event from which the time, location, and other relevant data of the event may be obtained.

In a broad sense, W4 message delivery allows a message (which may be any IO including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) to be delivered in accordance with delivery conditions based on any combination of the available W4 data types, including topical, spatial, temporal, and/or social data. Furthermore, because the W4 COMN coordinates delivery of messages via multiple communication channels and through multiple devices and other RWEs, it allows the communication channel for delivery of a message to be dynamically determined upon detection that the delivery conditions are met. Examples include a social alarm clock, place-based messages, social proximity-based messages, and time-shifted message delivery, to name but a few applications of the W4 message delivery functionality.

Predetermined sets of W4 delivery conditions can be packaged and provided to users in common bundles, e.g., a Parent's Package, a Boss' Package, a Vehicle Maintenance Package, etc. These bundles may include predetermined message content, delivery conditions and delivery condition templates that allow the users to quickly construct delivery conditions for messages that will be easily and clearly interpreted by the W4 COMN's message delivery subsystems.

FIG. 7A illustrates elements of an embodiment of a W4 engine adapted to perform W4 enhanced message delivery as described herein. The W4 engine 700 includes a correlation engine 506, an attribution engine 504 and an attention engine 508 as described above. The W4 engine 700 is provided with a message intake manager 702 that is adapted to receive messages and their associated delivery conditions from senders via the various communication channels interoperating with the W4 COMN. The W4 engine 700 further includes a message delivery manager 704, an identification engine 706 and a message enhancement manager 708.

As discussed above, it should be understood that multiple RWEs and IOs may be associated with a single message as a sender. For example, a user may create and send an email message with delivery conditions using a laptop computer. The user is an RWE having a unique W4 identifier. In addition, the laptop is an RWE with its own unique W4 identifier. The email application on the laptop may be tracked as an IO with its own W4 identifier. In an embodiment, some or all of the user, laptop computer and email application may be considered a sender of the that is the email message. In this case, the user may be considered the originating sender and the laptop and email application proxies for the originating sender. The concept of proxies was discussed above and is particularly important here where it is anticipated that human actors, either as senders, recipients or entities tied to a delivery condition, will be known to the W4 COMN primarily through information obtained from their proxies (e.g., proxy RWEs, such as their smart phones, computing devices, sensors, smart vehicles, home phones, etc., and proxy IOs such as email accounts, communication software, credit card accounts, data objects containing data generated by a RWE, data objects containing data about an RWE or event, etc.).

In an embodiment, this determination of the senders of a message, including determining who the user, if any, is that should be considered the original sender may be performed by the attribution engine 504 as described above. It should also be noted that some messages may be sent by a process programmatically, e.g., automatically during the course of the execution of a program, so that there is not a human sender to be identified but rather only a sender IO. Furthermore, the attribution engine 504 parses the received message and identifies W4 data for RWEs and IOs associated with the message. Alternatively, the attribution engine 504 may only identify the sending RWE that actually places the message into the W4 COMN and any other associated RWEs (e.g., proxies and/or originating sender) may be identified by the message intake manager 702 in conjunction with the correlation engine 506.

The W4 engine 700 also includes an identification engine 706 that is adapted to identify the message type and match the content output from the correlation engine 506 to the message IO. The identification engine 706 analyzes a series of possible augmentations in view of W4 entities within each message type.

The message intake manager 702, upon receipt of a message with delivery conditions, identifies the recipients and the delivery conditions of the message as described below. This may include requesting that the correlation engine 506 correlate the channel-specific identifiers of the recipients with other W4 data in order to identify a target human recipient, if any, and any proxies for that recipient. In addition, the same information may need to be determined for RWEs identified in the delivery conditions.

It should be understood that any human or non-networked entity that is a sender, recipient or subject of a delivery condition of a message may be identified only by proxy RWEs or IOs. For example, an email may be sent by or directed to “john.smith@yahoo.com” or a telephone call may be directed to “(720)555-0505.” In both cases, the identifiers used to identify the human (i.e., “john.smith@yahoo.com” and “(720)555-0505”) are identifiers of proxies of the actual intended human recipient. Based on the W4 data known to the W4 COMN, these identifiers of proxies may be analyzed, e.g., by the correlation engine 506, in order to determine the unique W4 identifier of the RWE that is accessed by, represented by or working through the proxy RWE or proxy IO. For example, “john.smith@yahoo.conm” and “(720)555-0505” may be communication channel-specific identifiers of RWEs or IOs that the W4 COMN is aware are proxies for a known human RWE (e.g., a user with the name John Smith) with a distinct unique W4 identifier.

A message enhancement manager 708 combines the matched content and the message IO Output from the identification engine 706 for delivery to the recipient. The W4 engine 700 further includes the message delivery manager 704 that controls the delivery of messages. In an embodiment, the message delivery manager 704 logs the delivery conditions for a message and monitors the W4 data for occurrence of the delivery conditions. When/if the delivery conditions are met, the message delivery manager 704 then delivers the message to the recipient. This may include selecting a delivery route or communication channel and selecting the appropriate proxy RWE (if applicable) for delivery of the message, possibly including reformatting the message for the selected RWE. For example, for an email message that is to be delivered to a recipient when that recipient is at a specified location, the email message may be reformatted as a text for display via a cellular phone or vehicle-mounted display device that is one of the recipient's proxy devices and delivered to that device when it is determined that the device is at the specified location. Similarly, voicemails that are to be delivered to a recipient when a certain team wins a game may be reformatted and transmitted to whatever proxy device the recipient may be using at the time that the team wins the game. Thus, the voicemail may be delivered to a cell phone number, a voicemail inbox, an email inbox as an attachment to an email, or at a work telephone number depending on what the recipient is doing at the time the team wins the game.

In order to determine when delivery conditions are met, the message delivery manager 704 may utilize the correlation engine 506 to monitor the W4 data. Furthermore, a determination that a delivery condition has been met may be possible only through the graphing and identification of relationships based on the correlations between IOs and RWEs known to the W4 COMN, as performed in the correlation engine 506. The relationships may be determined in response to a request to deliver a message, triggered by some other input, or may be automatically determined by the correlation engine on a periodic basis and stored for later use.

FIG. 7B illustrates an alternative embodiments of the aforementioned W4 engine 700 present in FIG. 7A. The W4 engine 700 interrelates with a content server 710. The content server 710, which may be one or multiple servers in one or more locations, is essentially a source of content, be it media content, ad content, graphics, video or text, that a third party is willing to pay a network operator or intermediary to have embedded in a message as part of an augmented message. In this manner the augmented message system can monetize specific types of content via the W4 engine 700. Examples of content are commercial data, advertising data, audio data, video data, literary data, and all other types of content that may be input into a digital message.

The additional content provides opportunities for monetization of the augmented messaging system described herein. Thus, for example, augmented content might take the form of advertising content or paid media placements that, in the context of the system described herein, would be highly targeted to the recipients based upon the context of the message, the information known about the recipients by the network or available to the network, as well as the relationship of sender to recipients or recipients to events described in the message. Thus, augmented messages could contain content by which the system administrator or operator that provides the augmented messaging platform can garner revenue.

Alternative embodiments of the content server 710 may position the content server 710 within or as part of the W4 Engine 700. In this arrangement, the content server 710 would interact directly with the message enhancement manager 708 during the combining message IO and enhanced content in preparation for delivery.

FIG. 8 illustrates an embodiment of a method for delivering messages over a network based on social, temporal, spatial and topical data for entities on the network. In the embodiment described below, depending on how the architecture is implemented, the operations described may be performed by one or more of the various components, engines and managers described above. In addition, sub-engines may be created and used to perform specific operations in order to improve the network's performance as necessary.

As described above, a foundational aspect of the W4 COMN that allows for conditional message delivery is the ongoing collection and maintenance of W4 data from the RWEs interacting with the network. In an embodiment, this collection and maintenance is an independent operation 899 of the W4 COMN and thus current W4 social, temporal, spatial and topical data are always available for use in testing of delivery conditions. In addition, part of this data collection operation 899 includes the determination of ownership and the association of different RWEs with different IOs as described above including prioritization among specific groups or sub-groups of RWEs and IOs. Therefore, each IO is owned/controlled by at least one RWE with a known, unique identifier on the W4 COMN and each IO may have many associations with other RWEs that are known to the W4 COMN.

In the embodiment shown, the method 800 is initiated when a message with a delivery condition is detected by the W4 COMN in a receive delivery request operation 802. Such a request may be generated by software on a computing device operated by a user, by an automated process or by a “dumb” device such as a cellular phone or a sensor. As discussed above, one or more RWEs may be identified as a sender of the message. Such identifications may be made from the data of the message, the source of the request or a combination of both. In addition, deductions may be made concerning a user that is the sender of the message based on W4 data for the known device or software senders of the message, as previously described.

The delivery request will further identify one or more recipients of the message. As discussed above, each recipient may be identified by a channel-specific identifier of a proxy RWE of the recipient. Thus, similar to the situation with senders, there may be multiple recipients associated with a message. For example, a recipient of an email may be identified as “bill.smith@yahoo.com”, which is an email address for a electronic mail account. Using the W4 data, it may be determined that the user associated that email address has multiple proxies on the W4 COMN including for example the email account identified by “bill.smith@yahoo.com”, a mobile telephone identified with a telephone number, a home telephone identified by a different telephone number, a toll payment transponder identified by a transponder identification number, a car identified by a license plate, an internet protocol (IP) address, a business telephone identified by a third telephone number, and a home address identified by one or more physical location coordinates or addresses. In an embodiment, requests to deliver a message to a recipient that is determined to be a proxy for a user (or other RWE such as a business or location) may be interpreted as requests to deliver the message to the user (or other RWE) that is accessible via the proxy as discussed below.

In an embodiment, the delivery condition may be part of the delivery request or may be included in an IO (e.g., as data or metadata) that constitutes the message. In such a situation, the address string or in formation is associated with the IO that is to be delivered. Such address string or address information may also be a part of the IO to be delivered. A request may also occur upon detection of an address string, such as for example, a user entering an address string into a field in an email composition screen or speaking an address string into a microphone on a device.

In an embodiment, the delivery conditions may be designated by the sender of the message, which may be an RWE, typically a user, or an IO, such as a process executing on a computing device. Any suitable way of selecting and associating the delivery conditions with the message may be used as long as the W4 COMN can identify the resulting data that embodies the delivery conditions. For example, for an email message the delivery conditions may be entered by the user into a delivery options interface provided by the email application, such delivery conditions then being stored as metadata of the message: this metadata then is decoded by the W4 COMN to identify the delivery conditions. For a telephone call, a message delivery system could use a voice or keypad data entry system to allow the caller to assign or select delivery conditions to the voice message from a audible menu. Delivery conditions may also be automatically generated and added to messages, for example by an application such as a parental control application that sends messages upon the detection certain activities or content. Other methods of associating delivery conditions with a message are possible and any suitable method may be used with the embodiments of the systems and methods described herein.

In the method 800, the RWEs and IOs may be identified in the delivery conditions by any identifier, unique or non-unique, communication channel-specific or global, as long as the identifier can be resolved by the W4 COMN to its intended target RWE or IO. Resolving channel-specific identifiers can be done by correlating the channel-specific identifier with other W4 data. Non-unique identifiers (e.g., identifiers such as “Mom”, “father”, “Debby”, “Bob”, “Starbucks”) may have to be disambiguated based on the W4 data known about the sender and the message to be delivered and any suitable disambiguation method may be utilized for this purpose.

In the embodiment, the message with delivery conditions may be considered to be detected when it is received from the sender(s), although the reader will understand that the message may not have actually been sent at the time of receipt of the delivery conditions. It is anticipated that under most circumstances that any attributable sender will already be known to the W4 COMN and provided with a unique W4 identifier as well as at least one communication channel-specific address (which is another form of unique identifier).

As mentioned above, the receive delivery request operation 802 may include receiving an actual IO (e.g., message file or snippet of text) from an RWE or an IO such as a email software being executed by an RWE, the IO to be transmitted as directed by the address string or pointer to an IO at another address or location. The IO may contain data such as the text or contents of the communication as well as additional information in the form of metadata. The data contained may be evaluated in order to identify the delivery conditions, additional RWEs associated with the message (e.g., people listed in text of a message but that are neither the sender nor a recipient), other IOs (e.g., hyperlinks to IO, attachments, etc.) contained in the message, and any topics discussed in the message.

The receive delivery request operation 802 may be considered to occur at any point in the delivery chain within the W4 COMN, e.g., by any one of the engines used to conduct IO intake, routing or delivery. For example, depending on how the implementers of the W4 COMN choose to implement the network functions, a message may be received and initially analyzed and information routed to the correlation engine and addressing engine by any one of the message intake and generation manager, user profile manager, message delivery manager or any other engine or manager in the W4 COMN's communication delivery chain.

After detection of delivery conditions associated with a message, the delivery conditions are analyzed in a delivery condition identification operation 804. The delivery condition identification operation 804 includes identifying each RWE in the delivery condition and what the actual delivery conditions are with respect to the RWEs. This may require parsing a string containing the delivery conditions or some other analysis of the data or metadata for the message. For example, an IO may be emailed by a sender addressed to a recipient (identified by an email address) with the delivery condition that the message should only be delivered when the recipient is at/with a specified RWE (e.g., another person, a location such as a park, or a business such as a grocery store, Laundromat, a coffee shop, etc.). In this example, the delivery condition identification operation 804 will identify the recipient, the specified RWE and a maximum distance or range of distances between the two that indicates the delivery condition is met (which may or may not be explicitly provided in the delivery condition).

As discussed above, the recipients and any RWEs in the delivery conditions may be proxies for users or other RWEs. The identification operation 804 includes determining whether the recipient and delivery condition RWEs are proxies or the actual target of the message. In addition if a specified RWE is a proxy, the identification operation 804 further includes identifying any RWEs that may be used as proxies for the specified RWE and for any RWEs for which the specified RWE is, itself, a proxy.

For example, given the relationships described in FIG. 1, a sender of an IO may specify an email address for the user 102 as a recipient and a delivery condition that the recipient (again identified by the email address) and user 144 (which may also be identified by some proxy identifier such as telephone number or email address) must be together (i.e., co-located). By retrieving the W4 data for the email address, it can be determined that it is a proxy For the user 102 and that the user 102 has many other proxies that could be used to identify the location of the user 102 including the car 106 and cell phone 104. The identification operation 804 would further identify the IOs (e.g., IOs 122, 124, 126) from which the current location information may be obtained for each of the identified proxies for the current location of the user 102. The process is repeated for the user 144, thus identifying the user 144 from the RWE identifier provided in the delivery conditions, if user 144 has any proxies, and where to obtain current location information for each of the proxies.

The identification operation 804 may distinguish between proxies based on the data available for the proxy and the delivery condition when identifying the proxies to be used to determine if a delivery condition is met. For example, a work email account for a user may be a proxy for the user, but if no current location data may be derived from user's use of the email account (e.g., the email account may be accessed from multiple devices and/or from multiple or any location), the work email account may not be identified as a proxy for the location of the user. If, however, the device the user uses to access the work email account at any given time can be identified and its location can be determined (e.g., a public computer on a network that the user uses to access his email account), then the device could be used as a proxy of the location of the user by virtue of its current relationship with the user's email account, even though the device may never have been used by the user before.

In an embodiment, the W4 engine may assume that for any identified RWE that is explicitly designated as a proxy for another RWE, that the other RWE is the intended entity and substitute it for identified RWE. For example, if an IO is emailed by a sender addressed to a recipient (identified by an email address) with the delivery condition that the message should only be delivered when the recipient is at/with a specified RWE, the IO may be delivered when it is determined that the recipient's cell phone (e.g., a proxy for the recipient, but not necessarily a proxy for the recipient's email account) is close enough to the specified RWE's cell phone (e.g., a proxy for the recipient) and the IO may also be delivered when it is determined that both the specified RWE and the recipient RWE are attending the same meeting/event based on message traffic, financial data (e.g., confirmation of event ticket purchase or detection of concurrent sales made at same location) or smart calendar entries.

The identification of proxies, as discussed above, may be explicit (e.g., designated as proxies by their associated user or RWE) or implicitly determined based on an analysis of W4 data. As discussed above, such W4 data may have been collected from messages, communications and IOs previously obtained or handled by the W4 COMN via many different communication channels and systems, including email and text-based communication channels as well as any communication channels that include audio data including channels that support telephone, voice over internet protocol (VOIP), and video communications such as video chat.

To determine inplicit proxies, the W4 data may be graphed in order to determine what RWEs are related and how and from this information make probabilistic assumptions about the nature of the relationships between RWEs. In an embodiment, correlations are made for and between each of the RWEs known to the W4 COMN based on the social data, spatial data, temporal data and logical data associated with each RWE. In one sense, the graphing operation may be considered a form of comparing the retrieved social data, spatial data, temporal data and logical data for all RWEs to identify relationships between RWEs and other contextual similarities.

It should be noted that the determination of implicit proxies may be performed each time a delivery condition is tested. This allows for the dynamic determination of the appropriate proxy for any RWE at any time. For example, during the week a corporate car or corporate cell phone may be considered a good proxy for the location of a recipient; but during the weekend a personal cell phone or personal car may be considered a better proxy for the recipient than the work cell phone.

After the RWEs, including any proxy RWEs, that may be used to confirm the occurrence of the delivery conditions are identified, the delivery condition identification operation 804 then identifies one or more data sources for the data necessary to test the delivery conditions. For example, if the delivery condition has a location requirement, data sources for the current location of the recipient and the specified RWEs will be identified. If the delivery condition has a temporal requirement, a system clock or the local time for the recipient and the specified RWEs may be identified. If the delivery condition has a status or state-related condition (e.g., a condition based on some identified current sensor readings or other condition such as current traffic conditions, current weather conditions, current speed, what an RWE is currently doing, current weather forecast, occurrence of a defined event, etc.), the appropriate data source or sources are identified that contain the current information necessary to test these conditions so that the message can be delivered upon determination that the current state of the RWE matches the specified state identified by the delivery condition.

For example, if the recipient is a user with a cell phone, the cell phone may be identified as the proxy for the current location of the recipient. The identification operation 804 will then identify the location of the data source from which the current location of the cell phone may be obtained. Such a data source, for example, may be maintained by the cellular service provider and accessible through their network. Alternatively, the cell phone may be provided with a GPS locator and the current location may be accessible from the cell phone itself. If the specified RWE is a business, a proxy RWE may not be needed but a proxy IO that contains information about the business including its current location(s) may be identified.

The delivery condition identification operation 804, as mentioned above, further includes identifying what constitutes the delivery condition(s) being met. For example, if the delivery condition requires an RWE be at a location, a range of distances between the RWE and identified location that, as far as the W4 COMN is concerned, will be treated as being “at” the location, Such a range may be predetermined by the operators of the W4 COMN (e.g., “at” is defined as within 10 meters), provided or otherwise selected by the sender as part of the delivery conditions or dynamically determined based on the data available and the precision of the location data or the application package or requirements.

If the delivery condition has a temporal requirement, a location may need to be identified if the sender designated only a relative time (e.g., Thursday at 6:00 am or Christmas) as opposed to an absolute time (Thursday, Nov. 8, 2007, 6:00 am Mountain Standard Time). The W4 COMN may, unless otherwise specified, assume all tines are local to the sender or the recipient and, when future times are designated imprecisely, the sender may be prompted for more information or the next possible match may be used.

If the delivery condition has a status or state-related condition, the appropriate condition is quantified so that it can be tested using the data from identified data source or sources. Again, the exact condition may have been specified by the sender (e.g., “deliver if winds greater than 40 mph detected at home”) or the condition may need to be determined from a less specific designations (e.g., “deliver if high winds detected at home”). For example, a delivery condition based on “heavy traffic” at a location will identify what constitutes heavy traffic in terms of the metrics monitored by and accessible from the identified traffic sensor. Thus, a threshold or range of values that will be considered to be “heavy traffic” when detected may be identified or a definition retrieved. As another example, the delivery condition “when the recipient is playing computer games” may be determined by identifying what software constitutes computer games and determining if the software is active at any particular moment. Furthermore, a delivery condition's quantification may not be the same for every instance of the delivery condition, but rather may be independently determined based on the current context and W4 data.

If the identification operation 804 can not adequately identify any of the parameters, RWEs and IOs described above or identify the delivery conditions to the extent sufficient to determine when a delivery condition has been met, the sender may be notified and asked for additional clarifying information. For example, in such a situation the W4 COMN may respond by prompting the sender of the message with a question such as, “By ‘deliver to debby at the grocery store’, do you mean your wife Deborah or your sister Deborah?” The prompt may include information derived from previous communications or other W4 data to assist the sender in confirming the proper identification of the recipient and delivery condition.

In addition, in an embodiment the delivery conditions may be very specifically designated by the sender when the message was created. For example, a sender may direct the W4 COMN to deliver a message only to the recipient via the recipient's cell phone, when the recipient's car and cell phone both are detected near a specified location at the same time so that when the sender can be certain that the message will not be delivered when, for instance, the recipient cycles by the gas station with the recipient's cell phone or when the sender drives by the location in the recipient's vehicle. This allows the sender to be very specific in defining delivery conditions, if that is what is desired or necessary for proper delivery of the message the way the sender intended.

After the delivery condition(s) and recipient(s) have been identified as described above, the delivery conditions are tested in a testing operation 806. This will include inspecting the necessary data sources and comparison of the various data elements (e.g., current location, temperature, state, etc.) as needed to determine if the delivery condition is met. In an embodiment, testing may include retrieving or requesting data from the identified data sources. The testing operation 806 may require only simple comparisons, e.g., comparing two values, such as current locations, to determine if the values are within a specified range. The testing operation 806 may be more complicated such as requiring complex calculations or simultaneous testing of multiple conditions related to a plethora of different RWEs and may include gathering and analyzing data from external sources.

Based on the results of the testing operation 806, a determination operation 808 determines if the delivery condition(s) are met or not. If the conditions are not met, then the delivery conditions are retested by repeating the testing operation 806. By such repeated testing the W4 data is monitored for occurrence of the delivery conditions. The retesting may be done periodically on a fixed schedule or dynamically in response to external conditions such as the receipt of new data on the W4 backbone related to RWEs implicated by the delivery conditions. The retesting may be done in perpetuity until the conditions are met or for a predetermined maximum time period specified by the sender or the W4 COMN. If a message cannot be delivered before the maximum time period is reached, the sender may be notified that the message was not delivered due to the delivery conditions not being met, e.g., “Message not delivered because Debby did not visit the grocery store within the specified period.”

If the determination operation 808 determines that the delivery conditions are met, the message is then transmitted to the recipient(s) in a delivery operation 810. In an embodiment, if the identified recipient is a proxy RWE, the message may be delivered to the proxy RWE regardless of the conditions identified that met the delivery condition. For example, an email may be sent to an identified email address when the cell phone of the user associated with that email address is detected at the delivery condition location. However, in this situation it is possible that the user may not receive the email until later if the cell phone is not email-enabled.

Alternatively, the method may select a communication channel and RWE to deliver the message to based on the conditions that triggered the delivery. For example, if a message is an email and the delivery condition is the recipient being at a location, upon detection that the recipient's cell phone is at the location the message may be reformatted for the recipient's cell phone, such as into an IM or SMS message, and transmitted to the cell phone via the cellular communication network servicing the cell phone. In such an embodiment, not only the identified proxy RWE of a recipient but all other proxies for the recipient (or other RWE) are considered possible delivery routes to the recipient. This allows the W4 message delivery system to select the most appropriate delivery route/communication channel/proxy combination when finally delivering the message, regardless of what delivery route/communication channel/proxy was initially identified as the recipient or used by the sender to create and send the message to the W4 COMN.

Such a dynamic message delivery system allows the delivery of message by alternative but effective means. For example, based on W4 data the cell phone of a user's best friend may be selected as a proxy for the user (as well as for the best friend) and a message to the user may be automatically delivered to the best friend's cell phone when delivery conditions are met and it is determined that the user and the best friend's cell phone are co-located. Under these circumstances the message delivery method can quickly and effectively deliver messages by communication channels and to devices that are completely unknown to the sender. As another example, when traveling, email messages for a user may be automatically delivered to coworkers with the user if the user does not have email access.

In addition, in an embodiment the delivery channel and RWE may be specifically designated by the sender when the message was created. For example, a sender may direct the W4 COMN to deliver a message only to the recipient via the car, when the car is detected near a gas station so that when the recipient cycles by the gas station with the recipient's cell phone delivery of the message is not triggered. Alternatively, the sender may identify that the W4 COMN dynamically select the communication route and recipient proxy device so that the message is delivered to the recipient in the manner that is most likely to get the message to the intended recipient user (as opposed to proxy for the user) upon occurrence of the delivery conditions.

As described above, the W4 COMN network contains and tracks, continuously, updated information regarding the senders and recipients that are RWEs on the W4 COMN. Thus, based on the information known to the W4 COMN regarding each RWE sender and recipient, a message sent by a first RWE sender would appear differently when received by each RWE recipient, based upon information known to the W4 COMN and utilized to augment each message passed through the W4 COMN to each intended RWE recipient. Thus, by way of example, a sender of a message to two recipients inviting them to dinner at a Japanese restaurant may type an e-mail addressed to recipient one and recipient two that states: “Let's do dinner Thursday night at Nobu”. Based upon user profile information known to the W4 COMN, recipient one may be known to be an aficionado of sake. This may be based on explicit user profile information obtained via communications related to the subject of sake, or frequent visits to websites offering information related to sake, profile information or tags associated with a user's profile on social networking sites known to the W4 COMN, purchase information recorded by the W4 COMN related to purchases of sake at wine shops, or search queries wherein recipient one has done extensive searching or research related to sake.

Accordingly, the message originally transmitted by sender to recipient one would be received on the W4 COMN, parsed to extract information concerning the subject of the message, as well as the identification of intended recipients. Based on the parsed content and intended recipients, and by way of non-limiting example, the W4 COMN would extract information related to the recipients and the subject such that recipient one would received an invitation to dine at Nobu that also included information about the restaturant's extensive sake menu, including links to various types of sake featured in the menu, along with pricing. Additionally, by way of further non-limiting example, a map to the location of the restaurant could also be included, and a link to obtain directions from the recipient's home, given that recipient one is a known RWE on the W4 COMN. Alternatively, the W4 COMN could, in analyzing information about recipient one, identify the time of the intended dinner meeting as occurring during a time where recipient one is normally at work, and alternatively provide directions from recipient one's work location.

On the other hand, recipient two may be a vegetarian or have other special meal requirements. This information could be known to the W4 COMN in similar manner to the description of the information known concerning recipient one's affinity for sake. Accordingly, recipient two's message would not contain information relating to sake, but rather, would contain information related to vegetarian items available on the restaurant's menu, along with a map to the restaurant from a different location, since information pertaining to recipient two known to the W4 COMN, for example from recipient two's calendaring application, identifies the night of the proposed dinner as occurring after recipient two's tennis lesson, and thus, directions from the tennis court to the restaurant would be included in the augmented message.

As in the above example, the augmented message is created through the information in the sender's profile available to the W4 COMN, the receivers' respective profiles, and the content of the message. The augmentation of the message will occur, preferably, utilizing the most current information about the senders and the recipients known to the W4 COMN at the time the message is created and intended to be sent. A message can be augmented based on a user's declared interests, which will determine how the augmented message is ultimately presented. The various users on the network that are identified as senders and recipients can have messages augmented in as complex or as simple a manner as the information about the sender or recipient that is known to the system augmenting the message. Thus, an intended recipient that has substantial amounts of information such as photos, literary interests, food interests, purchase history, calendar and location information, as well as a variety of different devices identified with that individual, has a richer opportunity to receive augmented messages than a person with less information available to the messaging network. Nonetheless, to the extent that the system can derive any declared interest or implicit interest information from a user's past interactions with the network or systems accessible to the network, such information would be available to be utilized as part of the augmentation process.

Additionally, the ultimate format in which the messages received could be personalized based upon a variety of factors. Thus, as described above, a user RWE can interact with the W4 COMN via a variety of devices. Additionally, those devices may be in different locations at different times. If at the time a message is sent by a sender, an intended recipient is on their cell phone, this fact can be known to the W4 COMN, based upon usage information gathered by the cell, or recognition that the user is logged on to the system via their cell phone and not via their personal computer at the time the message is to be sent. Accordingly, a further element of context that can be utilized to augment the message is the actual device that will be utilized by a recipient to receive and view the augmented message. In other embodiments, the system augments the message differently if it was intended to be received on a user's cell phone, versus via a TV versus via a web-browser.

Thus, in the broadest sense, the present disclosure relates to the augmenting of messages sent by senders to intended recipients, the augmenting occurring via a communication network that contains, or has access to information regarding the sender and recipients (or recipients alone), so that each augmented message contains the richest content possible, based upon a parsing of the message content, the identification of the various sender/receiver pairs implicated by the message, the modeling of social relationships between the senders and recipients and among the individual recipients, the identification of social structures or other data known or accessible by the network regarding the recipients, and the utilization of such information to supplement the original message created by the sender with augmented information that will be of high value to the recipient upon receipt of the augmented message.

In some instances, messages may be structured, such as messages that themselves contain metadata. In such case, the parsing of the message content would leverage any metadata found in the message, the metadata then acting as an additional information resource that can be utilized by the message augmentation network to seek additional content for use in creating the augmented messages to be sent to intended recipients.

Thus, while certain examples above have been given in the context of W4 COMN, it is apparent from the disclosure herein that the systems and concepts disclosed may be utilized in networks other than W4 COMN, such as for example, in social networks that provide internal messaging or message boards, electronic mail systems that have the ability to gather information concerning intended recipients and senders, photo sharing sites, or other network-connected user communities wherein user information can be extracted or obtained or stored, either explicitly or implicitly, for utilization in augmenting messages. In a further broad sense, the disclosure relates to a form of message that may be parsed to extract message content and sender and recipient information. Further information on intended recipients is then accessed and then analyzed, and the result of the analysis utilized to incorporate other information into the message so as to augment the message to contain information not originally sent by the sender, but augmented by the system based upon declared or extrapolated interests or requirements of the recipients.

Continuing one example above, once a message has been parsed and the recipient's information analyzed, the types of information that may be included in the augmented message are limited only by the potential size constraints of the message to be send and/or the additional resources that may be available to the network for use in augmented message such as, in the example above, photographs of the restaurant interior, reviews of the restaurant from a variety of sources available either on the network or the worldwide web at large, links to related information such as gas stations or parking lots in close proximity to the restaurant, videos containing interior footage, or, as described above, maps that can be tailored based on known information about the recipient's location at the time of message receipt or at the time of the intended meeting or subject of the message.

By way of further example, if the intended recipient has a physical need for information in a different format, such as, for example, Braille, or is physically limited in some way, such as wheelchair-bound, the system can augment the message to include a text-to-speech version of the message content, or information related to public transportation that can accommodate wheelchairs and that travel a route proximate the intended meeting place.

The augmentation of messages described herein can be as robust as the information available to the system utilized to augment the messages. Thus, as stated earlier, the more information related to intended recipients that is stored by or available to the message system, the more resources that are available to be applied by the messaging system in making determinations as to which augmented content should be included in a message. However, irrespective of the amount of information available to the system, the messaging system according the present disclosure is intended to operate in a manner such that a set of rules is defined for parsing message content, and identifying recipients. The parsed content is utilized to form a set of variables associated with the message, which variables are used to extract or obtain information related to the intended recipients. The information obtained is matched based on constraints designed into the system, in accordance with known constraint system methodologies, to map retrieved data concerning the recipients and identify relationships between the intended recipients and the message content in order to match augmented or enhanced content with the message content, and provide a new message that contains content not present in the original message created by the sender.

For example, the parsed data from a message can be analyzed and data related to the parsed message and recipients and obtained by the network, for use and creation of an augmented message. The augmented message may be created by the application of style sheets or templates that recognize or resolve message content against available data to yield highly pertinent information to the parsed message data. Thus, if the message contains information about a place, maps will be included. If the message, for example, relates to an event, a route or directions can be included based upon the location of the event and the location at the recipients, either at the time of the receipt of the message or at the time of the proposed event, depending on the nature and extent of information available about the recipients.

Thus, for classes of entities and message types, various solutions can apply. The constraint and resolution process takes into account the type of message and available data and, through a series of iterations, creates an augmented message that, preferably, takes into account the status of the sender or receiver and other factors that could influence what the augmented communication content should be.

Additionally, conditions such as the type of device upon which the intended recipient will be viewing the message can be taken into account in creating the augmented message. The number of iterations through which the variables and applied constraints are processed can vary as a matter of design choice, however, it will be recognized that more robust augmentation of messages can occur when the message parsing is more granular and the recipient data is more robust.

FIG. 9 illustrates an embodiment of W4 enhanced messaging 900, in accordance with the present disclosure. (As used herein the terms enhanced and augmented are used interchangeably to represent a message that is changed in accordance with the systems and methods described herein). W4 enhanced messaging 900 occurs during ongoing data collection by W4 COMN 902, which is continuous, as described above. The process of W4 enhanced messaging 900 begins when a message request for transmittal of a message sent by a sender is received in step 904. Once this occurs, the message is parsed to extract message content (which forms a W4 information object or IO), and W4 data relating to RWEs and IOs associated with the message are correlated and retrieved in step 906. The message is then graphed and/or mapped to the retrieved W4 data to identify relationships between the RWEs and IOs that are in association with the message IO in step 908. Accordingly, the message is classified per a type of message and consequently matched to enhanced content in regards to the message IO in step 910. In step 912, the delivery conditions are then analyzed to determine if the requirements of W4 COM-N protocol have been reached, and if so, the message proceeds to the next step; but if not, there is a feedback loop back to step 908 for further W4 data retrieval and identification. The message IO is subsequently combined with the enhanced content in preparation for delivery as a composite IO in step 914, which composite IO is the augmented message. The communication channel and/or format is then selected wherein the composite IO is delivered to, or alternatively when sent to the recipient RWEs 916. The process ends at step 918 upon delivery, or it may end when the augmented message is sent.

FIG. 10A illustrates another embodiment of a system of delivering messages over a network to any number of recipients, in an embodiment that is not implemented in accordance with the W4 COMN. The enhanced messaging system 1000 includes a transmitter 1002, a messaging system 1004 and recipients 1006(1-n). Messaging system 1004 has access to data sources 1003, which may be on the same network as messaging system 1004 or may be available on a global network such as the world wide web. Data sources 1003 are sources of information about the sender and recipients of a message to be sent through the messaging system, as well as sources of information about the content of the message. Data sources 1003 may be social networking sites, subscription services, calendaring systems, websites, music sharing sites, or other network accessible sources of information about the messaging system users and/or message content from which data can be extracted and analyzed for use in developing an augmented message as described herein.

As discussed in reference to FIG. 10A, a message and/or messages are transmitted from sender 1002 through the messaging system 1004, for subsequent delivery to the recipients 1006(1-n). During this communication, the message is parsed in a manner discussed above to extract message content and sender and receiver information or just receiver information. Messaging system 1004 accesses data sources 1003 to find information pertinent to the parsed message, and using the techniques described above extracts variables that may be associated with obtained data to resolve a series of constraints in order to create a new message having augmented content pertaining to each recipients 1006(1-n) known or derived profile or characteristics or preferences. Thus, the creation of an enhanced message occurs within the messaging system 1004.

Upon the messaging system 1004 receiving the message, elements of the message are parsed and data related to the recipients and the message, are retrieved. The retrieved data is mapped in order to correlate between the appropriate recipients and message content. The messaging system 1004 retrieves individual information related to each recipient 1006(1-n) for use in augmentation of the message. Accordingly, a specific enhanced message, which is augmented with data pertinent to each recipient 1006(1-n), is delivered to each recipient 1006(1-n). The messaging system 1004 may also have access to individual and/or personal information of each recipient 1006(1-n), as provided to the system 1004 by the recipients in the form of explicit preference data and social network affiliations.

FIG. 10B illustrates an alternative embodiments of the aforementioned enhanced messaging system 1000 present in FIG. 10A. The messaging system 1004 interrelates with a content server 1008. The content server 1008 is a source, or a representation of multiple sources, of additional types of content that are available for incorporation into the enhanced message produced by the messaging system 1004. Types of such content, by way of non-limiting example, are commercial data, advertising data, audio data, video data, literary data, and all other types of monetizable content that may be input into a digital message, for example advertising content or paid media placements that, in the context of the system described herein, would be highly targeted to the recipients 1006(1-n). based upon the context of the message, the information known about the recipients by the network or available to the network, as well as the relationship of transmitter 1002 to recipients 1006(1-n). or recipients to events described in the message, as discussed above.

One non-limiting example of a type of monetized content input in the enhanced message prior to delivery entails the sender 1002 inviting a group of recipient to play golf. One of the recipients 1006(1-n) has a very strong interest in golf such interest being derived from information discovered through the data retrieved from data sources having information concerning that particular recipient. Accordingly, the messaging system 1004 augments data pertaining to the sport golf, for example: a coupon for golf clubs at a local store, prior to the message being delivered to the recipient 1006(1-n) having the derived interest in golf. The local store then pays the network provider for such placement in the augmented message.

Thus, as discussed above, augmented content might take the form of advertising content or paid media placements that, in the context of the system described above, would be highly targeted to the recipients based upon the context of the message, the information known about the recipients by the network or available to the network, as well as the relationship of sender to recipients or recipients to events described in the message. Thus, augmented messages could contain content by which the system administrator or operator that provides the augmented messaging platform can garner revenue.

In Alternative embodiments, the content server 1008 is part of the enhanced messaging system 1000.

FIG. 11 illustrates a non-limiting example 1100 of augmented messages transmitted from the sender/transmitter 1102 to three recipients 1104, 1106, 1108. Within this example, the message is a party invitation. As described herein, the message is parsed and information and message type determined for each of the three recipients 1104, 1106, 1108, each of whom are detected to be utilizing a different type of communication/reception device. Recipient 1104 is on a mobile device, recipient 1106 is using a television set and recipient 1108 is using a computer system. Based upon each recipients' 1104, 1106, 1108 relevant personal data, the message is augmented to include enhanced content particular to each of them. These three examples portray the flexibility of the augmented message delivery options pertaining to alternative recipients using different devices.

In this example, the sender 1102 sends out a message pertaining to a party invite at the destination Café Bebo. Per obtained data relevant to each recipient 1104, 1106, 1108, the original message is augmented with recipient specific content. Each recipient 1104, 1106, 1108 receives a different augmented message modified as appropriate to that recipients' particular known or derived characteristics, likes/dislikes, location, and/or profile, (based on any available data, including topical, spatial, temporal, and/or social data), as well as the communication channel and format pertaining to the type of reception device, as discussed above.

Recipient 1104 receives the personally enhanced message with augmented content in the form of digital imagery related to the party location, along with a map from the current location of recipient 1104. Per this example, recipient 1104 receives the message augmented with photographs of Café Bebo and directions from their current location, which could be derived for example from user profile data or be derived from the sensed location of the mobile device of recipient 1104.

Recipient 1106, watching a television set or similar video device, which may include typical peripherals which would understood by one of ordinary skill in the art, receives the augmented message containing video content and personally relevant data. Recipient 1106 receives a different message augmented to include video content pertaining to the party invite and location, i.e. Café Bebo. Also, as the system has determined based on data about recipient 1106, that recipient 1106 does not have a car but is an avid biker, public transportation schedules and bike routes information is provided. Also, as recipient 1106 is known to the system to be a wine enthusiast, links to wine reviews about wines available on the menu at the Café are also included in the augmented message personal to recipient 1106.

Recipient 1108 is logged in on a computer or PC (or similar device), and receives a different augmented message pertaining to social and topical content relevant to recipient 1108, known to the system as a vegetarian. The personalized augmented message includes vegetarian menu options available at Café Bebo as extracted from Café Bebo's website, and also includes links to magazine reviews of the vegetarian courses served. Recipient 1108 also receives a video from sender Joe of imagery of Café Bebo.

Thus by way of non-limiting example, the content included in each augmented message varies so as to be specific and highly relevant to each recipient 1104, 1106, 1108 (per this example) and the communication device used.

In other embodiments, it is possible that the augmented message can change over time as conditions related to one or more recipients change after creation of the message. Thus, for example, if a message is augmented and transmitted to recipient one based on a set of conditions at the time the message was delivered, and yet the message was not read by recipient one, the system may take note of that fact and that recipient one has changed locations. The system can then reprocess the message based on the new information obtained about recipient one's changed location, and either send a new message or automatically replace the old message that was not read with the new message containing updated information. Thus, for example, if the original message contained directions to an intended meeting pace based on the system's knowledge that recipient one was in location A, and yet recipient one changes locations to location B prior to reading the first message, a second message can be created containing directions from location B to the intended meeting place. Thus, it is possible to provide continuing improved augmentation of messages by obtaining feedback related to the messages and the state of the messages.

As depicted in FIG. 9A, a step 960 is included to determine if a message recipient's status has changed since a message was transmitted, or if a message reply containing new information was sent. If so, the method returns to step 906 to take into account the new information concerning the message of recipient status to create and send a further augmented message.

Thus, by way of further non-limiting example, and in another exemplary context, many social networking sites or professional relationship sites contain invitations to intended recipients that are not yet members of the sender's social network. Such invitations frequently go unanswered. In such a case, if the invitation message was sent as part of the augmented messaging system described herein, the system could monitor the fact that the invitation was never responded to and send a further augmented message containing new information that might further induce the recipient to change their decision not to respond to the invitation.

By further way of non-limiting example, the system could monitor replies to messages that contain information from a recipient to the sender and other recipients, and augment the reply in similar manner to incorporate additional content that might be pertinent to the information added by the recipient that is now the sender of the reply message. Thus, based upon changes in the status of recipients, or changes in the content of the message as result of additions made by recipients, or based upon changing information relative to the sender, the system can continuously augment and either resend or replace augmented messages as conditions of the senders and/or receivers change.

As the W4 COMN continuously monitors communications taking place via or known to the network, data is created that can be utilized to effectively identify groups or clusters of communications that are meaningful in a particular context, or meaningful in particular circumstances, or meaningful to individual participant RWEs. In other words, as IOs are created and processed to extract W4 relationships, messages or communications can be extracted and clustered to form a cohesively linked subset of communications that are topically, socially, temporally, and/or spatially related, even if they are independently formed and from disparate communication channels. By contrast, in typical communication patterns such as emails, as multiple messages get created, sent, responded to and/or forwarded, a “thread” is created that contains all communications in a particular chain of communications, which are linked by subject header, communication participants, and messaging flow. This thread is highly susceptible to breakage however, and fails to include messages about the same topic that might have been sent through other channels such as SMS, MMS, IM, phone call, webcast, photo or video sharing, for example. Standard communication threads are not only susceptible to breakage due to utilization of different channels of communication, but a change in the wording of the subject header of a message (which may still be topically or otherwise coherently related to a previous message with a different subject header) and/or a change in the communication handle of the sender and/or receiver (e.g., email address, IM handle, phone number, etc,) can break the messaging thread as well. A cluster of messages as described more fully herein aggregates any number of related communications over disparate channels into a cohesive unit of related and readily accessible information. In other words, by way of non-limiting analogy, a cluster is like a continuous conversation related to a topic, which conversation can grow more information rich over time.

In a network such as W4 COMN, because communications are analyzed for spatial, temporal, social, and topical information based not only on message content but the senders and recipients and channel type, to name a few parameters, a significant amount of data is available for analysis about messages regardless of their being connected (or not) in a particular actual thread. Thus, messages (IOs) sent by multiple users (RWEs) from disparate devices (RWEs or proxies) can be parsed as described herein in relation to message content, sender and receiver information, or temporal relatedness (sequentially, periodicity, duration, or other measures of temporal similarity) and/or spatial relatedness (contiguity, containment, or other measures of spatial similarity) of IOs or RWEs, to extract data that can be analyzed using known clustering algorithms to find related concepts or topics therein. These measures of relatedness can be applied to all components of the W4 Communications system (senders, receivers, messages and their metadata and contents, sending and delivery triggers, etc.) and may themselves have combined and/or second order patterns of similarity, such as the temporal and/or other patterns of interactions among the components of the system (such as cumulative and/or average volume of communications, frequency of communications, frequency of communications, etc.), the topology of the messaging flow and changes therein, temporal patterns of spatial changes, temporal patterns of topical changes, co-occurrence of topics, times, and places, etc. These messages are then identified as part of a cluster of related messages that can be dynamically created, and dynamically modified as new messages are analyzed, which clusters are topically related in a meaningful way to one or more, or all, message senders and recipients. Thus, using the W4 data extraction methodologies described herein, messages passed through or known to the W4 COMN can be logically and meaningfully clustered into actionable groups of related messages. The boundaries of the cluster are set using the W4 data related to the candidate messages being analyzed, and parameters can be set to draw the cluster boundaries using known clustering algorithms.

For example, all messages related to a particular event might be candidate messages for a cluster, yet some messages might fall outside certain boundary parameters, either because the message is too early or to late in relation to the event, or because the sender is too far from the event, or because the sender is known only to one other person among the senders and receivers of the messages in the cluster, or because the message mentions a different context for the event (e.g., parking at the event, while all other messages relate to dining at the event). The messages utilized to form clusters, in an embodiment, may be augmented messages as described above, and the augmentation process described above can utilize information related to messages associated with clusters in further augmenting messages created by or to be received by RWEs associated with a particular cluster, or with IOs related to a particular cluster.

By way of non-limiting example, assume that W4 RWE “Jerry” is attending the global “ZBX conference” in Las Vegas. Jerry sends multiple messages to multiple people via email, SMS, and voice calls on his cell. Some of his emails and calls relate to a speech he is giving at the conference. Some of his texts and emails relate to a dinner he is trying to set up at the conference, and some of his emails, texts and calls relate to a round of golf he is trying to organize. Of the three events, some recipients receive messages related to all three, others only two of the events, others only one of the events. Because the W4 COMN monitors and parses all of Jerry's messages (IOs) and has knowledge of the RWE recipients and their locations and interrelationships, the network can extract and analyze the information in and about Jerry's messages and organize them into meaningful clusters such as “Jerry ZBX speech”, “Jerry ZBX dinner” and “Jerry ZBX golf”. The data used to build these clusters comes from the messages themselves as well as data available on or to the W4 COMN that is related to the message content, senders and receivers, and any related triggers. The data may be analyzed using known algorithms (e.g., K-means clustering, hierarchical clustering, etc.) to Cluster related objects in an n-dimensional space, whose dimensions represent W4 metadata, content data, and may also include composite and/or second order features upon which to cluster. The algorithm can be adjusted in accordance with known techniques to optimize the distance between objects based on unsupervised machine learning principles or supervised machine learning techniques, whether by system operators or by actual user feedback (for example dragging a message into or out of a cluster can provide information to be used by the algorithm to fine tune it). Thus, known algorithms incorporating feedback techniques to reinforce and improve the quality of the created cluster are preferably utilized in embodiments, although embodiments without such an enhancement are also contemplated as apparent from the teaching herein.

Clusters can be contained in other clusters, or overlap with other clusters, or be separated or unique from other clusters. Continuing with the above example, Jerry's golf cluster could contain Jerry's dinner cluster but only partially overlap with Jerry's speech cluster.

Clusters are also to an extent personal, but can be shared among users, either by permission or convention. For example. Jerry's ZBX golf cluster might be shared by the members of the group that is golfing, but Jerry can chose to limit access to his Jerry ZBX speech cluster. In embodiments, clusters that share the same content can be differently labeled depending on the owner of the cluster. So a cluster for Mary's ZBX golf cluster that contains the same or virtually all of the same IOs as Jerry's ZBX golf cluster would appear to Mary as Mary's ZBX golf cluster, and as discussed further below regarding the cluster becoming an actionable item, may cause different data to be displayed or actions to be performed when the cluster is acted upon by Mary, due to Mary's differing social or temporal or spatial relationships to the RWEs that are related to the IOs associated with Mary's ZBX golf cluster.

It is preferable, but not necessary, that the cluster itself becomes an actionable IO or super IO made up of many IOs, which when applied to other contexts can provide rich and meaningful information. With reference to the above example, assume Jerry is trying to locate the people (RWEs) associated with Jerry's ZBX golf cluster to estimate when they will arrive at the golf course and to set up teams, and assume Jerry is using a device and his golf cluster is represented by an information object on a display, such as a draggable window that is labeled “Jerry's ZBX golf”. In one example of how a cluster can be actionable, if Jerry drags his golf cluster to a map of Las Vegas, the location of the people in the cluster can be displayed on the map at their then-present location as extracted by the W4 COMN (from cell phone location or other sensor data for example), along with information about their interrelationships as extracted from social network data, so Jerry can set up teams based who knows who and when they will arrive.

Another example of the cluster being actionable is for items in the cluster to be selectable, that is, active items (IOs representing RWEs contained within and/or related to the cluster) that are clickable or selectable in a user interface. By clicking on or otherwise selecting an item in a cluster, other items related to that item can appear, so that related entities or information objects can be readily identified. Moreover, because the system takes into account the time and place in which the action is occurring, the action that results from the interaction with a cluster can change based on the time or location of the person interacting with the cluster. Thus if it is a week before the golf outing and Jerry clicks on his golf cluster, Jerry might get a list of who has responded to invitations to play, since the system knows the actual date for the golf game is a week away and Jerry has been sending numerous email invitations. On the other hand, as described above, interacting with the cluster on the day of the outing would yield a different result, such as the map described above or a list of invitees that need to rent golf clubs.

Not only can the W4 components of a cluster (IOs representing RWEs such as people, places, times, objects, topics, and events) can be made actionable as an entire cluster, but individual W4 components of a cluster and/or subsets of W4 components may be made actionable as well.

Because the W4 COMN has access to so much rich data concerning the RWEs and IOs that are related to any cluster, the system can optionally provide a list of choices to a user upon interaction with a cluster to better inform the system about the action to take. Thus, continuing with the example above, if Jerry clicks on his ZBX golf cluster, the system may return a list of options from which Jerry can select to better inform the system about the information Jerry needs from the cluster. By way of non-limiting example, the system would return prompts like “invitees”, “maps”, “contacts” “nearby restaurants” or “weather” for Jerry to select from, and return different information according to each selection.

Additionally, other information available to the W4 COMN and related to items in a Cluster can advise the cluster building algorithm to define a cluster as it is being created or to refine an existing cluster. Communications made through other channels, for example non-W4 mail services or IM services, could be analyzed by the system once they become known to the system, by hitting an inbox or message queue for example, and that information can be used by the system for refinement purposes, for example by making a connection between a non-W4 mail address and a third party mail address, thus disambiguating and augmenting the representation of RWEs known to the system. In particular, while embodiments have been described in relation to a W4 COMN, the systems, functions and methods described herein can be applied to other message networks, such as depicted ion FIGS. 10A and B, or legacy messages of other networks can be imported into the W4 COMN for analysis and inclusion.

Thus in the context of the above example, assume RWE “Jim-” is mentioned in a message relating to golf. That alone may be insufficient for the algorithm to include Jim in Jerry's golf cluster. But assume Jim's social network data available to or on the W4 COMN identifies Jim as an avid golfer. Such additional information informs the algorithm to include Jim as a member of Jerry's golf cluster.

As information from messages is parsed and related data extracted and analyzed and clusters formed, the system must determine how to summarize and label the cluster contents. Standard summarization techniques can be used to summarize the contents of a cluster, yet the cluster needs to be labeled. One methodology for creating a label for a cluster is to utilize known methods to find representative terms found in the messages in the cluster. One known method is term frequency inverse document frequency (TFIDF). Of course such a method can be augmented by other data or limits, such as for example temporal limits, so even though a good cluster label might be “Jerry's golf” as identified using TFIDF, a better label might be “‘Jerry’s Saturday golf” if Jerry has more than one round scheduled over multiple days. Other methods of conceptual clustering combined with natural language syntax algorithms can convert the representative W4 data of a cluster into a linguistically intelligible label for the cluster. These summarization and labeling algorithms also can utilize information about the hierarchical and/or other relationships of the clusters when determining the best label for any given cluster,

As described herein, opportunities for monetization of the clustering techniques shown and described herein exist. In a similar manner as described above in the context of augmented messages, content that third parties are willing to pay to have included in clusters can be made available to the system either as part of the W4 COMN or as external content sources accessible to the W4 COMN. As a result of the correlation function that identifies relationships between messages and related IOs and RWEs, and in embodiments the summarization and labeling functions, a great deal of relevant information is derived about a cluster. This information forms the basis for matching the relevant information to similarly relevant content that a third party is willing to pay to have included in or available to the cluster. By way of continuing example, as part of the building of Jerry's ZBX golf cluster, the system can recognize, analyzing contextual information in Jerry's messages as described herein, and include a special offer from the golf course that becomes apparent to Jerry when he takes action on the cluster, such as checking invitee status as described above. Alternatively, the system can recognize other potential recipients for augments messages as described herein based upon the relationship of the potential recipient to Jerry's golf cluster.

With reference to FIG. 12, a flowchart is depicted for describing an example of a method of an embodiment for creating message clusters as described herein. In step 1202, ongoing data collection in the W4 COMN is an ongoing process. As messages pass through or become known to the W4 COMN in step 1204, they are parsed and analyzed to retrieve data related to the RWEs and IOs associated with the message, in step 1206. In step 1208, and as described in detail in other sections herein, the retrieved data is graphed or mapped so that relationships among the RWEs and IOs identified as related to the parsed message(s) can be correlated and collected for association with the message. In step 1210, using the relationships identified in the prior steps, related messages are identified based upon the identified relationships between RWEs and IOs associated with the message. In step 1212, clusters of messages are formed based upon the relationships between messages identified in step 1210, in accordance with rules for building clusters that are established as a matter of system design and implementation, and take into account such considerations as, by way of non-limiting example, a degrees of relatedness computation, user preferences or constraints, or other temporal, social or spatial relationships that can be identified as variables and resolved using a rule set, template, or other constraint resolution technique.

In step 1214 the contents of the messages in the cluster are summarized using known summarization techniques to establish a corpus of content of the cluster that can be further analyzed or refined. By way of example, steps 1210 through 1214 can be iterative to the extent that additional passes yield higher confidence results. In step 1216, the message content, or preferably the summarization, is analyzed to determine a “label” or identifying name, graphic, symbol or icon for the cluster that may be viewed and interacted with by the user on a RWE device such as a personal computer, PDA or smart phone.

Once a label is created and viewable, and with reference to FIG. 13, the label may be displayed on a device display as a user interface element, in step 1310. As described elsewhere herein, a user may interact with the label, by, for example, clicking or dragging, to perform a further action or seek further information, in step 1312, depending on the action selected, or upon analysis of the action by the system, the user may be provided with options to provide further feedback prior to or after the action being performed. If no such option is presented, or if no response is received, as detected in step 1314, in step 1316 the action is performed based upon an analysis of relationships between RWEs and IOs associated with the cluster.

If in step 1318 user feedback is detected, the action selected is performed based upon an analysis of relationships between RWEs and IOs associated with the cluster and the user feedback.

With reference to FIG. 7C, additional components of the W4 engine 700 are depicted for carrying out the processes described herein relative to the creation of and interaction with clusters. Components not discussed but depicted are as discussed above in relation to FIGS. 4-6 and 7A and 7B. As described above, messages (or other communications, referred to for simplicity herein as messages) are parsed and analyzed and correlations established between the message content and senders and receivers and other data available to the system to establish relationships between messages and RWEs and IOs in the W4 COMN. As related messages are identified (using techniques described herein, such as for example an n-dimensional space model or vector model), a cluster building engine 730, which may perform the functions of identifying related messages or may do so in cooperation with attribution engine 504 and correlation engine 506 described herein, collects the related messages and forms and tracks clusters of these related messages.

As the cluster forms, or in alternate embodiments after a cluster is formed, a summarization engine 730 summarizes the contents of the messages in a cluster using standard summarization techniques. Once the cluster is formed and summarized, a labeling engine 740 analyzes the cluster contents to derive a label for the cluster that is representative and contextually meaningful. This label may then be displayed on a user device in the form of an interface element or graphic, text, icon or shape, for example, to visually represent the cluster to the user and to provide an element through which the label, and in turn the cluster, can be interacted with by the user.

As described herein, the system can be configured to make the clusters and their components actionable by the user, or to accept and process user feedback related to desired actions on the cluster. Cluster action engine 750 monitors the systems for actions taken on or with cluster labels and, as appropriate to the detected action, will request further feedback from the user or process the action, depending on whether such feedback is detected.

Content server 710, which represents one or multiple content sources such as advertisement servers or other media or content suppliers from which payment can be had, as described elsewhere, are connected to, or in embodiments part Of, W4 engine 700. The function of identifying opportunities for monetizeable content placement and communicating with content server 710 to obtain such content can be performed in cluster building engine 730 or cluster actions engine 750, or can be a distributed function performed among a combination of other components, such as correlation engine 506.

FIGS. 14A-14C depict one non-limiting example of a user having clusters related to various upcoming events, including the ZBX conference related events described in examples herein, as well as other events, for example, vacation and golf at Pebble Beach. As seen in FIG. 14A, the cluster labeled ZBX 1402 has sub-clusters labeled ZBX dinner 1404 and ZBX keynote 1406, depicting that clusters can be hierarchically arranged. The hierarchical arrangement of the cluster provides the participants in the cluster an organized portrayal of related events. In this embodiment, the cluster ZBX 1402 and sub-clusters ZBX dinner 1404 and ZBX keynote 1406 have already been created by the labeling engine 740 and delivered to a user's device via the message delivery manager 704, therefore they are being displayed on a user's device interface element 1408 (i.e., computer screen, cellular phone screen, PDA screen, etc.). In FIG. 14B, the user determines to take action upon the ZBX dinner cluster 1404 by dragging and dropping it to a map 1410, which depicts the city in which the subject dinner is to occur, in order to better identify a location and preferences of the people (RWEs) related to the ZBX dinner cluster 1404. The dragging and dropping actions, which are commonly know techniques, here signal the system to correlate the ZBX dinner cluster 1404 with the map 1410. As discussed above in reference to FIG. 13, the user may perform further actions or seek further information by further interaction with a cluster label. In FIG. 14C, in response to the dragging and dropping, the ZBX dinner cluster 1404 may be expanded to display to the user the participants of the dinner and additional data pertaining to each user and/or the cluster content. As in this embodiment, each of the participants in ZBX dinner cluster 1404 have preferred dinner location(s) 1412 which may be derived from each of their profiles. The cluster action engine 750, as described in one embodiment above, would perform further analysis and utilize social network and preference information to identify the restaurants and food types enjoyed by the proposed dinner participants in order to assist the user in picking a restaurant for the dinner, as seen in FIG. 14C.

Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, may be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible. Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that may be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently.

While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications may be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure. Numerous other changes may be made that will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the disclosure(s) disclosed. 

1. A method comprising: receiving a plurality of messages for delivery on a network, the messages each containing an identity of at least one recipient; parsing the received messages to extract message content; collecting recipient data available to the network by searching for social data, spatial data, temporal data and logical data relating to the message recipients and the extracted message content; analyzing the messages to identify relationships between the extracted message content and the message recipients; grouping the messages into a cluster based upon the identified relationships and a rule set, the cluster comprising messages that satisfy rules of the rule set; and analyzing the cluster to determine a cluster label for identification of the cluster to the message recipients.
 2. The method of claim 1 further comprising: transmitting the cluster to the message recipients; providing the message recipients the ability to provide feedback via interaction with the cluster label, wherein the cluster label is viewable to the message recipients upon transmittal.
 3. The method of claim 2 further comprising: determining if feedback is supplied by the message recipients.
 4. The method of claim 3 further comprising: upon detection of feedback supplied from the message recipients, performing an action on the cluster based on the identified relationships associated with the cluster and the feedback provided by the message recipients.
 5. The method of claim 3 further comprising: upon no feedback supplied from the message recipients, performing an action on the cluster based upon the identified relationships associated with the cluster.
 6. The method of claim 4, wherein the feedback supplied by the message recipients is based upon the message recipients performing at least one of clicking on or dragging the cluster label to seek additional information.
 7. The method of claim 1, wherein the rule set represents a constraint resolution technique for building clusters based on message content of the messages.
 8. The method of claim 7, wherein the constraint resolution technique is a degrees of relationship computation between the social data, spatial data, temporal data and logical data of the message recipients and a sender of one of the plurality of messages.
 9. The method of claim 7, wherein the rule set is based upon user preferences that identify the message content that is of interest to the message recipients.
 10. The method of claim 1, wherein the cluster is contained within a hierarchy of clusters.
 11. The method of claim 1, wherein the cluster overlaps with at least one other cluster.
 12. The method of claim 1, wherein the cluster is formed based upon a specific event.
 13. The method of claim 1, wherein the cluster is formed upon a time period.
 14. The method of claim 1, wherein the cluster is formed based upon a specific location or the at least one recipient.
 15. The method of claim 1, wherein the cluster is formed based upon a distance between the message recipients and sender.
 16. The method of claim 1 wherein the determining the cluster label further comprises: labeling the cluster pertaining to the message content of the cluster that is understood by each of the message recipients.
 17. The method of claim 16, wherein each of the message recipients have the option to alter the cluster label to a representation that is contextually meaningful for the user.
 18. The method of claim 6, wherein said seeking of additional information comprises: correlating the message cluster with at least one of the social data, spatial data, temporal data and logical data to identify refined content pertaining to at least one of the message content of the messages and other message recipients.
 19. A computer-readable medium tangibly encoded with instructions to perform a method comprising: receiving a plurality of messages for delivery on a network, the messages each containing an identity of at least one recipient; parsing the received messages to extract message content; collecting recipient data available to the network by searching for social data, spatial data, temporal data and logical data relating to the message recipients and the extracted message content; analyzing the messages to identify relationships between the extracted message content and the message recipients; grouping the messages into a cluster based upon the identified relationships and a rule set, the cluster comprising messages that satisfy rules of the rule set; and analyzing the cluster to determine a cluster label for identification of the cluster to the message recipients.
 20. The computer-readable medium of claim 19 further comprising: transmitting the cluster to the message recipients; providing the message recipients the ability to provide feedback via interaction with the cluster label, wherein the cluster label is viewable to the message recipients upon transmittal.
 21. The computer-readable medium of claim 20 further comprising: determining if feedback is supplied by the message recipients.
 22. The computer-readable medium of claim 21 further comprising: upon detection of feedback supplied from the message recipients, performing an action on the cluster based on the identified relationships associated with the cluster and the feedback provided by the message recipients.
 23. The computer-readable medium of claim 21 further comprising: upon no feedback supplied from the message recipients, performing an action on the cluster based upon the identified relationships associated with the cluster.
 24. The computer-readable medium of claim 22, wherein the feedback supplied by the message recipients is based upon the message recipients performing at least one of clicking on or dragging the cluster label to seek additional information.
 25. The computer-readable medium of claim 19, wherein the rule set represents a constraint resolution technique for building clusters based on message content of the messages.
 26. The computer-readable medium of claim 25, wherein the constraint resolution technique is a degrees of relationship computation between the social data, spatial data, temporal data and logical data of the message recipients and a sender of one of the plurality of messages.
 27. The computer-readable medium of claim 25, wherein the rule set is based upon user preferences that identify the message content that is of interest to the message recipients.
 28. The computer-readable medium of claim 19, wherein the determining the cluster label further comprises: labeling the cluster pertaining to the message content of the cluster that is understood by each of the message recipients.
 29. The computer-readable medium of claim 28, wherein each of the message recipients have the option to alter the cluster label to a representation that is contextually meaningful for the user.
 30. The computer-readable medium of claim 24, wherein said seeking of additional information comprises: correlating the message cluster with at least one of the social data, spatial data, temporal data and logical data to identify refined content pertaining to at least one of the message content of the messages and other message recipients.
 31. A computer system comprising: a plurality of processors; an attention engine implemented on one of the plurality of processors for receiving a plurality of messages for at least one recipient via a network, wherein the messages contain an identity of at least one recipient; an attribution engine implemented on one of the plurality of processors for extracting message content from the received messages; a message intake manager implemented on one of the plurality of processors for collecting recipient data available to the network by searching for social data, spatial data, temporal data and logical data relating to the at least one recipient and the extracted message content; a correlation engine implemented on one of the plurality of processors for correlating relationships between the extracted message content and the at least one recipient associated with the messages; a cluster building engine implemented on one of the plurality of processors for forming clusters via grouping the messages based upon the correlated relationships between the extracted message content and the at least one recipient; a summarization engine implemented on one of the plurality of processors for summarizing the content of the messages contained within each of the clusters formed by the cluster building engine; and a labeling engine implemented on one of the plurality of processors for analyzing the summarized cluster content produced by the summarization engine to determine a label, wherein the label provides a visual cluster summarization representation for the at least one recipient of the clusters.
 32. The system of claim 31 further comprising: a cluster action engine implemented on one of a plurality of processors for monitoring each cluster for actions taken on or with the cluster label, wherein upon detection of said action, requesting feedback in view of the clusters.
 33. The system of claim 31 further comprising: a content server implemented on a third party processor for producing additional content for input into the message, wherein the additional content is embedded in the message in exchange for payment to the third party.
 34. The system of claim 33, wherein the content server interacts directly with the cluster building engine during the forming of the clusters of the related messages.
 35. The system of claim 31 further comprising: a message delivery manager implemented on one of the plurality of processors for determining if a delivery condition is included in the clusters and communicating the clusters to the at least one recipient in accordance with the delivery condition being met. 